Top Data & Analytics Jobs

Designs, develops, and maintains data platforms, pipelines, and integration solutions across on-premises and cloud environments. Responsibilities include resolving data quality and security issues, building ELT processes, developing data scripts and relational or non-relational models, and monitoring data lake performance and scalability. The role also supports analytical and operational decision-making through reliable data solutions and collaborates with cross-functional teams.

Own and evolve business-critical data products, designing scalable data services, APIs, models, and batch or streaming pipelines. Lead projects through implementation, deployment, monitoring, and production support while applying AI tools throughout the software development lifecycle. Collaborate across engineering, product, data, and business teams, contribute to architecture and governance, improve reliability, and mentor earlier-career engineers.

Build and operate scalable growth data pipelines, models, feature datasets, and measurement systems supporting paid acquisition, ad bidding, experimentation, attribution, and audience targeting. Partner with Growth, Marketing, Analytics Engineering, and Data Science to deliver reliable data products, productionize machine-learning workflows, maintain data quality and observability, and improve platform performance, cost efficiency, and developer experience.

Architect, deploy, and operate core data platform services (Airflow, Superset, Terraform). Build CI/CD pipelines, automate with Python, manage cloud infrastructure, implement security and compliance, develop dbt data models, catalog assets in OpenMetadata, and mentor junior engineers.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

Design, build, optimize, and support scalable batch and real-time data pipelines and enterprise data products. Develop reusable frameworks, ingestion automation, testing, CI/CD, and data-quality solutions while ensuring governance, security, lineage, observability, and policy-compliant data segregation. Troubleshoot production issues, improve performance and cost efficiency, and collaborate with engineers, data stewards, scientists, product managers, and business stakeholders across global teams.

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

Designs, develops, tests, and maintains secure, scalable data pipelines and architectures. Performs data modeling, ETL, SQL and NoSQL development, statistical analysis, performance tuning, and troubleshooting. Builds production code using Python and PySpark in AWS, supports data controls and operational stability, and creates reporting and visualizations from large datasets. Applies Agile, CI/CD, TDD or BDD practices and responsibly uses AI-assisted development tools.

Leads enterprise data engineering architecture and SAP data conversion initiatives. Designs scalable ingestion, transformation, storage, and delivery patterns; builds BODS ETL workflows; manages extraction, cleansing, mapping, validation, reconciliation, and loading. Establishes automation, governance, quality, lineage, security, observability, and infrastructure-as-code practices. Coordinates analysts, SAP teams, engineers, business owners, vendors, and consultants through mock conversions, cutovers, rollback, and production operations while coaching teams and resolving critical issues.

Design, build, test, and maintain scalable, secure data pipelines and architectures. Update data models, advise on/configure tools, validate controls, use SQL/NoSQL, and apply enterprise AI capabilities to accelerate and validate data engineering workflows while ensuring traceability and data sensitivity handling.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.

Develop and implement data solutions, pipelines, architectures, analytics, reports, and dashboards for clients. Apply Python, Java, ETL, business intelligence, and advanced analytics techniques to interpret complex data, improve operations, and support strategic decisions. Collaborate with stakeholders, manage client relationships, identify data challenges, and deliver high-quality managed services work while contributing to continuous process improvement.

Designs and maintains financial data models and ETL/ELT pipelines using dbt Cloud and Snowflake. Partners with Finance, Accounting, Analytics, and business stakeholders to translate requirements into dimensional, fact, aggregation, and analytical models. Implements data quality controls, investigates discrepancies, optimizes SQL and dbt workflows, documents transformations, and improves access to trusted financial data through automation and self-service solutions.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and process distributed data in Databricks. Apply software engineering practices, review Git pull requests, partner with data scientists and stakeholders, translate requirements into Jira stories, and support data governance, quality, integrations, and deployment across the enterprise.

Build, optimize, and support scalable batch and streaming data pipelines for enterprise workforce, billing, and interaction datasets. Develop cloud-native solutions with AWS, Databricks, PySpark, Kafka, Kubernetes, and Airflow while processing tens of terabytes of data. Provide production support, performance tuning, troubleshooting, and data platform automation. Collaborate across Product, Governance, Analytics, and Engineering teams, support data warehousing and reliability practices, participate in architecture decisions, and mentor junior engineers.

Build and maintain scalable data and AI platforms, including data models, schemas, vector stores, knowledge bases, ETL/ELT pipelines, feature pipelines, embedding pipelines, and evaluation datasets. Integrate data systems with machine learning and LLM applications, support analytics and RAG systems, and implement security, privacy, and governance controls. Collaborate across leadership, engineering, finance, and data science teams to deliver reliable AI-ready data foundations.

Develop and optimize cloud-based data lakes and pipelines supporting analytics and machine learning. Build automated workflows using Python, SQL, Dagster, dbt, and comparable tools; transform raw data for reporting; and maintain secure, compliant handling of PII and PHI in a regulated SDLC. Collaborate with data scientists, product teams, and engineers to solve technical challenges and improve the data platform.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

Designs, builds, and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud-based technologies, optimizes existing architectures, improves data formats and accessibility, and collaborates with analytics teams and stakeholders to deliver reliable solutions for reporting and decision-making.