The Role
Build and lead enterprise BI, analytics, data engineering, and AI-augmented reporting solutions. Responsibilities include developing SQL, Python, and PySpark pipelines; creating Power BI, Tableau, and Looker dashboards; designing semantic and dimensional models; integrating LLM and RAG capabilities; performing advanced analytics and forecasting; implementing data quality and governance; advising stakeholders; managing delivery activities; and mentoring junior analysts.
Summary Generated by Built In
Job Description
BI Analyst / Senior Consultant – Business Intelligence & AI
Job Summary
We are seeking an experienced BI Analyst / Senior Consultant with 6–9 years of hands-on expertise in business intelligence, advanced analytics, and data engineering — combined with growing exposure to AI/LLM application development. The ideal candidate brings strong proficiency in SQL, Python, and PySpark for data processing, alongside deep BI platform expertise in Power BI or Tableau. They will have experience building governed semantic layers, developing scalable data pipelines, and integrating Large Language Model (LLM) capabilities into analytics workflows. This role sits at the intersection of traditional BI and next-generation AI-augmented analytics, making it ideal for a technically strong consultant ready to lead complex data initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
- Write complex SQL queries, stored procedures, and optimized transformations across platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift.
- Develop and maintain scalable data pipelines using Python and PySpark for large-scale batch and near-real-time data processing.
- Build and manage ELT/ETL workflows using dbt, ADF, Airflow, or Fivetran to ingest structured and semi-structured data.
- Implement Spark-based data processing on Databricks or Azure HDInsight for high-volume analytics workloads.
- Optimize query performance through partitioning, clustering, caching strategies, and execution plan analysis.
BI Development & Reporting
- Design, develop, and deploy enterprise-grade dashboards and reports using Power BI (DAX, Power Query, Composite Models) and Tableau.
- Build semantic models, calculated measures, KPI frameworks, and row-level security (RLS) configurations in Power BI or Looker.
- Develop LookML models, explores, and views in Looker to expose governed data layers for self-service analytics.
- Optimize BI report performance through DirectQuery tuning, aggregation tables, and incremental refresh strategies.
- Lead and mentor junior analysts in BI development standards, DAX best practices, and data modeling techniques.
Semantic Layer & Dimensional Modeling
- Design and maintain enterprise semantic models using dbt Semantic Layer, Power BI Semantic Models, Cube.dev, or AtScale.
- Build dimensional models (Star Schema, Snowflake Schema) with fact and dimension tables optimized for analytical query patterns.
- Define and standardize reusable business metrics, KPIs, hierarchies, and dimensions across reporting platforms.
- Ensure metric consistency and single source of truth across BI, dashboards, and AI-driven outputs.
AI & LLM Application Development (Exposure Required)
- Develop or contribute to AI-powered analytics applications using LLM APIs such as OpenAI GPT-4, Azure OpenAI, or Anthropic Claude.
- Build Retrieval-Augmented Generation (RAG) pipelines using frameworks such as LangChain or LlamaIndex to enable natural language querying over structured and unstructured data.
- Integrate LLM-generated insights, AI summaries, and conversational BI interfaces into existing Power BI or Tableau reporting workflows.
- Use Python libraries (openai, langchain, transformers, sentence-transformers) to prototype and deploy AI-driven analytics features.
- Implement vector search and embedding-based retrieval using tools such as FAISS, Pinecone, or Azure AI Search to surface contextual data insights.
- Contribute to prompt engineering, fine-tuning strategies, and evaluation frameworks for LLM outputs in analytics contexts.
- Explore and apply AI-native BI capabilities such as Power BI Copilot, Tableau Pulse, and Looker Explore AI.
Advanced Analytics & Data Science Integration
- Perform exploratory data analysis (EDA) using Python (pandas, numpy, matplotlib, seaborn, plotly) to surface trends and business insights.
- Collaborate with data science teams to integrate ML model outputs (e.g., churn scores, forecasts, classification results) into BI reporting layers.
- Develop statistical analyses, cohort analyses, and A/B test result reporting to support business experimentation.
- Apply time-series analysis and forecasting techniques using Python (statsmodels, Prophet, scikit-learn) for business planning use cases.
Stakeholder Engagement & Consulting
- Act as a senior analytical advisor to business stakeholders, translating complex data findings into clear business narratives.
- Lead requirement-gathering workshops, solution design sessions, and stakeholder demos for BI and AI analytics initiatives.
- Document functional and technical specifications for data pipelines, semantic models, and BI solutions.
- Participate in agile delivery — sprint planning, stand-ups, retrospectives — and manage delivery timelines for analytics workstreams.
Data Quality & Governance
- Implement data quality frameworks using dbt tests, Great Expectations, or custom SQL-based validation rules.
- Maintain data lineage, documentation, and metadata cataloging using tools such as Microsoft Purview, Alation, or dbt Docs.
- Define and enforce data governance standards, access control policies, and compliance requirements across BI and data assets.
Required Skills
Programming & Query Languages
- SQL — Advanced: CTEs, window functions, query optimization, stored procedures, dynamic SQL
- Python — Proficient: pandas, numpy, matplotlib, seaborn, sqlalchemy, requests, pyspark
- PySpark — Experience with distributed data processing, DataFrame API, Spark SQL, and UDFs
- DAX — Advanced: calculated columns, measures, time intelligence, row-level security
- LookML — Experience building models, explores, and views in Looker
- Shell scripting / Bash for pipeline automation and environment management
BI & Visualization Platforms
- Power BI — Advanced: Desktop, Service, Dataflows, Composite Models, Deployment Pipelines
- Tableau — Proficient: calculated fields, LOD expressions, Tableau Prep, Tableau Server
- Looker / LookML
- Sigma Computing or ThoughtSpot (preferred)
Data Platforms & Cloud
- Snowflake — Warehouses, clustering, materialized views, Snowpipe, dynamic data masking
- Azure: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL
- AWS: Redshift, Glue, S3, Athena (preferred)
- GCP: BigQuery, Dataflow, Looker (preferred)
- Databricks — Delta Lake, Unity Catalog, MLflow (preferred)
Data Engineering & Integration Tools
- dbt (Core / Cloud) — models, tests, macros, seeds, snapshots, semantic layer
- Apache Airflow — DAG development, scheduling, operators
- Fivetran / Matillion / Azure Data Factory for data ingestion
- Apache Kafka or Azure Event Hubs for streaming data (preferred)
AI & LLM Technologies (Exposure Required)
- LLM APIs: OpenAI GPT-4 / GPT-4o, Azure OpenAI Service, Anthropic Claude
- LangChain or LlamaIndex for RAG pipeline development
- Vector databases: FAISS, Pinecone, Weaviate, or Azure AI Search
- Python AI libraries: openai, transformers, sentence-transformers, tiktoken
- Prompt engineering, context management, and chain-of-thought techniques
- Familiarity with AI-native BI tools: Power BI Copilot, Tableau Pulse, Looker Explore AI
- Microsoft Fabric or Azure AI Foundry exposure (preferred)
Semantic Layer Technologies
- dbt Semantic Layer / MetricFlow
- Power BI Semantic Models (Tabular / XMLA endpoint)
- Cube.dev or AtScale
- Snowflake Semantic Model (preferred)
DevOps & Delivery
- Git / GitHub / Azure DevOps — branching, pull requests, CI/CD pipelines
- Docker basics for containerized analytics environments
- Agile / Scrum delivery methodology
- JIRA / Azure Boards for sprint and backlog management
Preferred Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Snowflake SnowPro Core or Advanced: Data Engineer Certification.
- Databricks Certified Associate Developer for Apache Spark.
- dbt Certified Developer (preferred).
- Experience in a consulting, professional services, or client-facing delivery environment.
- Hands-on experience building end-to-end AI/LLM-powered analytics applications.
Nice to Have
- Experience with Microsoft Fabric (OneLake, Fabric Notebooks, Real-Time Analytics).
- Exposure to MLOps practices: model versioning, monitoring, and deployment pipelines using MLflow or Azure ML.
- Knowledge of Data Vault 2.0 modeling methodology.
- Experience with real-time streaming analytics using Kafka, Spark Streaming, or Azure Stream Analytics.
- Familiarity with graph databases or knowledge graphs for AI-enhanced search.
- Exposure to data observability tools such as Monte Carlo, Anomalo, or dbt Artifacts.
Key Competencies
- Technical depth with the ability to move fluidly between SQL, Python, PySpark, and BI tooling.
- Strong analytical thinking and data-driven problem solving.
- Excellent communication skills — ability to present complex technical findings to non-technical stakeholders.
- Business acumen and senior stakeholder management in consulting environments.
- Curiosity and adaptability toward AI/LLM technologies and emerging analytics platforms.
- Collaborative mindset across data engineering, data science, and business teams.
- Attention to detail in data quality, governance, and documentation.
- Leadership and mentoring of junior analysts and BI developers.
Success Criteria
The successful candidate will:
- Deliver scalable, high-performance data pipelines and BI solutions using SQL, Python, PySpark, and cloud-native tools.
- Build governed semantic models and KPI frameworks that serve as a single source of truth across the organization.
- Prototype and deliver AI/LLM-powered analytics features that enhance insight discovery and decision-making.
- Enable self-service analytics capabilities for business users through well-governed BI platforms.
- Drive data quality, lineage, and governance standards across analytics assets.
- Mentor junior team members and establish BI and analytics best practices across the delivery team.
Responsibilities
Job Description
BI Analyst / Senior Consultant – Business Intelligence & AI
Job Summary
We are seeking an experienced BI Analyst / Senior Consultant with 6–9 years of hands-on expertise in business intelligence, advanced analytics, and data engineering — combined with growing exposure to AI/LLM application development. The ideal candidate brings strong proficiency in SQL, Python, and PySpark for data processing, alongside deep BI platform expertise in Power BI or Tableau. They will have experience building governed semantic layers, developing scalable data pipelines, and integrating Large Language Model (LLM) capabilities into analytics workflows. This role sits at the intersection of traditional BI and next-generation AI-augmented analytics, making it ideal for a technically strong consultant ready to lead complex data initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
- Write complex SQL queries, stored procedures, and optimized transformations across platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift.
- Develop and maintain scalable data pipelines using Python and PySpark for large-scale batch and near-real-time data processing.
- Build and manage ELT/ETL workflows using dbt, ADF, Airflow, or Fivetran to ingest structured and semi-structured data.
- Implement Spark-based data processing on Databricks or Azure HDInsight for high-volume analytics workloads.
- Optimize query performance through partitioning, clustering, caching strategies, and execution plan analysis.
BI Development & Reporting
- Design, develop, and deploy enterprise-grade dashboards and reports using Power BI (DAX, Power Query, Composite Models) and Tableau.
- Build semantic models, calculated measures, KPI frameworks, and row-level security (RLS) configurations in Power BI or Looker.
- Develop LookML models, explores, and views in Looker to expose governed data layers for self-service analytics.
- Optimize BI report performance through DirectQuery tuning, aggregation tables, and incremental refresh strategies.
- Lead and mentor junior analysts in BI development standards, DAX best practices, and data modeling techniques.
Semantic Layer & Dimensional Modeling
- Design and maintain enterprise semantic models using dbt Semantic Layer, Power BI Semantic Models, Cube.dev, or AtScale.
- Build dimensional models (Star Schema, Snowflake Schema) with fact and dimension tables optimized for analytical query patterns.
- Define and standardize reusable business metrics, KPIs, hierarchies, and dimensions across reporting platforms.
- Ensure metric consistency and single source of truth across BI, dashboards, and AI-driven outputs.
AI & LLM Application Development (Exposure Required)
- Develop or contribute to AI-powered analytics applications using LLM APIs such as OpenAI GPT-4, Azure OpenAI, or Anthropic Claude.
- Build Retrieval-Augmented Generation (RAG) pipelines using frameworks such as LangChain or LlamaIndex to enable natural language querying over structured and unstructured data.
- Integrate LLM-generated insights, AI summaries, and conversational BI interfaces into existing Power BI or Tableau reporting workflows.
- Use Python libraries (openai, langchain, transformers, sentence-transformers) to prototype and deploy AI-driven analytics features.
- Implement vector search and embedding-based retrieval using tools such as FAISS, Pinecone, or Azure AI Search to surface contextual data insights.
- Contribute to prompt engineering, fine-tuning strategies, and evaluation frameworks for LLM outputs in analytics contexts.
- Explore and apply AI-native BI capabilities such as Power BI Copilot, Tableau Pulse, and Looker Explore AI.
Advanced Analytics & Data Science Integration
- Perform exploratory data analysis (EDA) using Python (pandas, numpy, matplotlib, seaborn, plotly) to surface trends and business insights.
- Collaborate with data science teams to integrate ML model outputs (e.g., churn scores, forecasts, classification results) into BI reporting layers.
- Develop statistical analyses, cohort analyses, and A/B test result reporting to support business experimentation.
- Apply time-series analysis and forecasting techniques using Python (statsmodels, Prophet, scikit-learn) for business planning use cases.
Stakeholder Engagement & Consulting
- Act as a senior analytical advisor to business stakeholders, translating complex data findings into clear business narratives.
- Lead requirement-gathering workshops, solution design sessions, and stakeholder demos for BI and AI analytics initiatives.
- Document functional and technical specifications for data pipelines, semantic models, and BI solutions.
- Participate in agile delivery — sprint planning, stand-ups, retrospectives — and manage delivery timelines for analytics workstreams.
Data Quality & Governance
- Implement data quality frameworks using dbt tests, Great Expectations, or custom SQL-based validation rules.
- Maintain data lineage, documentation, and metadata cataloging using tools such as Microsoft Purview, Alation, or dbt Docs.
- Define and enforce data governance standards, access control policies, and compliance requirements across BI and data assets.
Required Skills
Programming & Query Languages
- SQL — Advanced: CTEs, window functions, query optimization, stored procedures, dynamic SQL
- Python — Proficient: pandas, numpy, matplotlib, seaborn, sqlalchemy, requests, pyspark
- PySpark — Experience with distributed data processing, DataFrame API, Spark SQL, and UDFs
- DAX — Advanced: calculated columns, measures, time intelligence, row-level security
- LookML — Experience building models, explores, and views in Looker
- Shell scripting / Bash for pipeline automation and environment management
BI & Visualization Platforms
- Power BI — Advanced: Desktop, Service, Dataflows, Composite Models, Deployment Pipelines
- Tableau — Proficient: calculated fields, LOD expressions, Tableau Prep, Tableau Server
- Looker / LookML
- Sigma Computing or ThoughtSpot (preferred)
Data Platforms & Cloud
- Snowflake — Warehouses, clustering, materialized views, Snowpipe, dynamic data masking
- Azure: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL
- AWS: Redshift, Glue, S3, Athena (preferred)
- GCP: BigQuery, Dataflow, Looker (preferred)
- Databricks — Delta Lake, Unity Catalog, MLflow (preferred)
Data Engineering & Integration Tools
- dbt (Core / Cloud) — models, tests, macros, seeds, snapshots, semantic layer
- Apache Airflow — DAG development, scheduling, operators
- Fivetran / Matillion / Azure Data Factory for data ingestion
- Apache Kafka or Azure Event Hubs for streaming data (preferred)
AI & LLM Technologies (Exposure Required)
- LLM APIs: OpenAI GPT-4 / GPT-4o, Azure OpenAI Service, Anthropic Claude
- LangChain or LlamaIndex for RAG pipeline development
- Vector databases: FAISS, Pinecone, Weaviate, or Azure AI Search
- Python AI libraries: openai, transformers, sentence-transformers, tiktoken
- Prompt engineering, context management, and chain-of-thought techniques
- Familiarity with AI-native BI tools: Power BI Copilot, Tableau Pulse, Looker Explore AI
- Microsoft Fabric or Azure AI Foundry exposure (preferred)
Semantic Layer Technologies
- dbt Semantic Layer / MetricFlow
- Power BI Semantic Models (Tabular / XMLA endpoint)
- Cube.dev or AtScale
- Snowflake Semantic Model (preferred)
DevOps & Delivery
- Git / GitHub / Azure DevOps — branching, pull requests, CI/CD pipelines
- Docker basics for containerized analytics environments
- Agile / Scrum delivery methodology
- JIRA / Azure Boards for sprint and backlog management
Preferred Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Snowflake SnowPro Core or Advanced: Data Engineer Certification.
- Databricks Certified Associate Developer for Apache Spark.
- dbt Certified Developer (preferred).
- Experience in a consulting, professional services, or client-facing delivery environment.
- Hands-on experience building end-to-end AI/LLM-powered analytics applications.
Nice to Have
- Experience with Microsoft Fabric (OneLake, Fabric Notebooks, Real-Time Analytics).
- Exposure to MLOps practices: model versioning, monitoring, and deployment pipelines using MLflow or Azure ML.
- Knowledge of Data Vault 2.0 modeling methodology.
- Experience with real-time streaming analytics using Kafka, Spark Streaming, or Azure Stream Analytics.
- Familiarity with graph databases or knowledge graphs for AI-enhanced search.
- Exposure to data observability tools such as Monte Carlo, Anomalo, or dbt Artifacts.
Key Competencies
- Technical depth with the ability to move fluidly between SQL, Python, PySpark, and BI tooling.
- Strong analytical thinking and data-driven problem solving.
- Excellent communication skills — ability to present complex technical findings to non-technical stakeholders.
- Business acumen and senior stakeholder management in consulting environments.
- Curiosity and adaptability toward AI/LLM technologies and emerging analytics platforms.
- Collaborative mindset across data engineering, data science, and business teams.
- Attention to detail in data quality, governance, and documentation.
- Leadership and mentoring of junior analysts and BI developers.
Success Criteria
The successful candidate will:
- Deliver scalable, high-performance data pipelines and BI solutions using SQL, Python, PySpark, and cloud-native tools.
- Build governed semantic models and KPI frameworks that serve as a single source of truth across the organization.
- Prototype and deliver AI/LLM-powered analytics features that enhance insight discovery and decision-making.
- Enable self-service analytics capabilities for business users through well-governed BI platforms.
- Drive data quality, lineage, and governance standards across analytics assets.
- Mentor junior team members and establish BI and analytics best practices across the delivery team.
Qualifications
Job Description
BI Analyst / Senior Consultant – Business Intelligence & AI
Job Summary
We are seeking an experienced BI Analyst / Senior Consultant with 6–9 years of hands-on expertise in business intelligence, advanced analytics, and data engineering — combined with growing exposure to AI/LLM application development. The ideal candidate brings strong proficiency in SQL, Python, and PySpark for data processing, alongside deep BI platform expertise in Power BI or Tableau. They will have experience building governed semantic layers, developing scalable data pipelines, and integrating Large Language Model (LLM) capabilities into analytics workflows. This role sits at the intersection of traditional BI and next-generation AI-augmented analytics, making it ideal for a technically strong consultant ready to lead complex data initiatives.
Key Responsibilities
Data Engineering & Pipeline Development
- Write complex SQL queries, stored procedures, and optimized transformations across platforms such as Snowflake, Azure Synapse, BigQuery, or Redshift.
- Develop and maintain scalable data pipelines using Python and PySpark for large-scale batch and near-real-time data processing.
- Build and manage ELT/ETL workflows using dbt, ADF, Airflow, or Fivetran to ingest structured and semi-structured data.
- Implement Spark-based data processing on Databricks or Azure HDInsight for high-volume analytics workloads.
- Optimize query performance through partitioning, clustering, caching strategies, and execution plan analysis.
BI Development & Reporting
- Design, develop, and deploy enterprise-grade dashboards and reports using Power BI (DAX, Power Query, Composite Models) and Tableau.
- Build semantic models, calculated measures, KPI frameworks, and row-level security (RLS) configurations in Power BI or Looker.
- Develop LookML models, explores, and views in Looker to expose governed data layers for self-service analytics.
- Optimize BI report performance through DirectQuery tuning, aggregation tables, and incremental refresh strategies.
- Lead and mentor junior analysts in BI development standards, DAX best practices, and data modeling techniques.
Semantic Layer & Dimensional Modeling
- Design and maintain enterprise semantic models using dbt Semantic Layer, Power BI Semantic Models, Cube.dev, or AtScale.
- Build dimensional models (Star Schema, Snowflake Schema) with fact and dimension tables optimized for analytical query patterns.
- Define and standardize reusable business metrics, KPIs, hierarchies, and dimensions across reporting platforms.
- Ensure metric consistency and single source of truth across BI, dashboards, and AI-driven outputs.
AI & LLM Application Development (Exposure Required)
- Develop or contribute to AI-powered analytics applications using LLM APIs such as OpenAI GPT-4, Azure OpenAI, or Anthropic Claude.
- Build Retrieval-Augmented Generation (RAG) pipelines using frameworks such as LangChain or LlamaIndex to enable natural language querying over structured and unstructured data.
- Integrate LLM-generated insights, AI summaries, and conversational BI interfaces into existing Power BI or Tableau reporting workflows.
- Use Python libraries (openai, langchain, transformers, sentence-transformers) to prototype and deploy AI-driven analytics features.
- Implement vector search and embedding-based retrieval using tools such as FAISS, Pinecone, or Azure AI Search to surface contextual data insights.
- Contribute to prompt engineering, fine-tuning strategies, and evaluation frameworks for LLM outputs in analytics contexts.
- Explore and apply AI-native BI capabilities such as Power BI Copilot, Tableau Pulse, and Looker Explore AI.
Advanced Analytics & Data Science Integration
- Perform exploratory data analysis (EDA) using Python (pandas, numpy, matplotlib, seaborn, plotly) to surface trends and business insights.
- Collaborate with data science teams to integrate ML model outputs (e.g., churn scores, forecasts, classification results) into BI reporting layers.
- Develop statistical analyses, cohort analyses, and A/B test result reporting to support business experimentation.
- Apply time-series analysis and forecasting techniques using Python (statsmodels, Prophet, scikit-learn) for business planning use cases.
Stakeholder Engagement & Consulting
- Act as a senior analytical advisor to business stakeholders, translating complex data findings into clear business narratives.
- Lead requirement-gathering workshops, solution design sessions, and stakeholder demos for BI and AI analytics initiatives.
- Document functional and technical specifications for data pipelines, semantic models, and BI solutions.
- Participate in agile delivery — sprint planning, stand-ups, retrospectives — and manage delivery timelines for analytics workstreams.
Data Quality & Governance
- Implement data quality frameworks using dbt tests, Great Expectations, or custom SQL-based validation rules.
- Maintain data lineage, documentation, and metadata cataloging using tools such as Microsoft Purview, Alation, or dbt Docs.
- Define and enforce data governance standards, access control policies, and compliance requirements across BI and data assets.
Required Skills
Programming & Query Languages
- SQL — Advanced: CTEs, window functions, query optimization, stored procedures, dynamic SQL
- Python — Proficient: pandas, numpy, matplotlib, seaborn, sqlalchemy, requests, pyspark
- PySpark — Experience with distributed data processing, DataFrame API, Spark SQL, and UDFs
- DAX — Advanced: calculated columns, measures, time intelligence, row-level security
- LookML — Experience building models, explores, and views in Looker
- Shell scripting / Bash for pipeline automation and environment management
BI & Visualization Platforms
- Power BI — Advanced: Desktop, Service, Dataflows, Composite Models, Deployment Pipelines
- Tableau — Proficient: calculated fields, LOD expressions, Tableau Prep, Tableau Server
- Looker / LookML
- Sigma Computing or ThoughtSpot (preferred)
Data Platforms & Cloud
- Snowflake — Warehouses, clustering, materialized views, Snowpipe, dynamic data masking
- Azure: Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL
- AWS: Redshift, Glue, S3, Athena (preferred)
- GCP: BigQuery, Dataflow, Looker (preferred)
- Databricks — Delta Lake, Unity Catalog, MLflow (preferred)
Data Engineering & Integration Tools
- dbt (Core / Cloud) — models, tests, macros, seeds, snapshots, semantic layer
- Apache Airflow — DAG development, scheduling, operators
- Fivetran / Matillion / Azure Data Factory for data ingestion
- Apache Kafka or Azure Event Hubs for streaming data (preferred)
AI & LLM Technologies (Exposure Required)
- LLM APIs: OpenAI GPT-4 / GPT-4o, Azure OpenAI Service, Anthropic Claude
- LangChain or LlamaIndex for RAG pipeline development
- Vector databases: FAISS, Pinecone, Weaviate, or Azure AI Search
- Python AI libraries: openai, transformers, sentence-transformers, tiktoken
- Prompt engineering, context management, and chain-of-thought techniques
- Familiarity with AI-native BI tools: Power BI Copilot, Tableau Pulse, Looker Explore AI
- Microsoft Fabric or Azure AI Foundry exposure (preferred)
Semantic Layer Technologies
- dbt Semantic Layer / MetricFlow
- Power BI Semantic Models (Tabular / XMLA endpoint)
- Cube.dev or AtScale
- Snowflake Semantic Model (preferred)
DevOps & Delivery
- Git / GitHub / Azure DevOps — branching, pull requests, CI/CD pipelines
- Docker basics for containerized analytics environments
- Agile / Scrum delivery methodology
- JIRA / Azure Boards for sprint and backlog management
Preferred Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Microsoft Certified: Power BI Data Analyst Associate (PL-300).
- Snowflake SnowPro Core or Advanced: Data Engineer Certification.
- Databricks Certified Associate Developer for Apache Spark.
- dbt Certified Developer (preferred).
- Experience in a consulting, professional services, or client-facing delivery environment.
- Hands-on experience building end-to-end AI/LLM-powered analytics applications.
Nice to Have
- Experience with Microsoft Fabric (OneLake, Fabric Notebooks, Real-Time Analytics).
- Exposure to MLOps practices: model versioning, monitoring, and deployment pipelines using MLflow or Azure ML.
- Knowledge of Data Vault 2.0 modeling methodology.
- Experience with real-time streaming analytics using Kafka, Spark Streaming, or Azure Stream Analytics.
- Familiarity with graph databases or knowledge graphs for AI-enhanced search.
- Exposure to data observability tools such as Monte Carlo, Anomalo, or dbt Artifacts.
Key Competencies
- Technical depth with the ability to move fluidly between SQL, Python, PySpark, and BI tooling.
- Strong analytical thinking and data-driven problem solving.
- Excellent communication skills — ability to present complex technical findings to non-technical stakeholders.
- Business acumen and senior stakeholder management in consulting environments.
- Curiosity and adaptability toward AI/LLM technologies and emerging analytics platforms.
- Collaborative mindset across data engineering, data science, and business teams.
- Attention to detail in data quality, governance, and documentation.
- Leadership and mentoring of junior analysts and BI developers.
Success Criteria
The successful candidate will:
- Deliver scalable, high-performance data pipelines and BI solutions using SQL, Python, PySpark, and cloud-native tools.
- Build governed semantic models and KPI frameworks that serve as a single source of truth across the organization.
- Prototype and deliver AI/LLM-powered analytics features that enhance insight discovery and decision-making.
- Enable self-service analytics capabilities for business users through well-governed BI platforms.
- Drive data quality, lineage, and governance standards across analytics assets.
- Mentor junior team members and establish BI and analytics best practices across the delivery team.
Skills Required
- 6-9 years of hands-on experience in business intelligence, advanced analytics, and data engineering
- Advanced SQL, including CTEs, window functions, query optimization, stored procedures, and dynamic SQL
- Proficiency in Python, including pandas, NumPy, Matplotlib, Seaborn, SQLAlchemy, requests, and PySpark
- Experience with PySpark distributed processing, DataFrame API, Spark SQL, and UDFs
- Advanced DAX skills, including calculated columns, measures, time intelligence, and row-level security
- Experience building LookML models, explores, and views in Looker
- Shell scripting or Bash experience
- Advanced Power BI experience with Desktop, Service, Dataflows, Composite Models, and Deployment Pipelines
- Proficient Tableau experience, including calculated fields, LOD expressions, Tableau Prep, and Tableau Server
- Experience with Looker and LookML
- Experience with Snowflake and cloud data platforms
- Experience with Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and Azure SQL
- Experience with dbt, including models, tests, macros, seeds, snapshots, and semantic layer
- Experience developing Apache Airflow DAGs, schedules, and operators
- Experience with Fivetran, Matillion, or Azure Data Factory for data ingestion
- Exposure to LLM APIs such as OpenAI, Azure OpenAI, or Anthropic Claude
- Experience with LangChain or LlamaIndex for RAG pipeline development
- Experience with vector search technologies such as FAISS, Pinecone, Weaviate, or Azure AI Search
- Experience with Python AI libraries, prompt engineering, context management, and LLM evaluation
- Experience designing semantic models and dimensional models using star or snowflake schemas
- Experience with Git-based development, CI/CD, Docker, Agile/Scrum, and Jira or Azure Boards
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field
- Microsoft Certified Power BI Data Analyst Associate certification
- Snowflake SnowPro Core or Advanced Data Engineer certification
- Databricks Certified Associate Developer for Apache Spark certification
- dbt Certified Developer certification
- Consulting, professional services, or client-facing delivery experience
- Hands-on experience building end-to-end AI or LLM-powered analytics applications
- Experience with Microsoft Fabric, MLOps, Data Vault 2.0, real-time streaming, graph databases, or data observability tools
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The Company
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
