Top Data & Analytics Jobs

Leads enterprise data engineering and AI architecture, strategy, roadmaps, governance, and delivery across multiple teams. Designs scalable Azure, Microsoft Fabric, Databricks, Power BI, data mesh, lakehouse, data product, and AI platforms. Oversees generative AI, RAG, vector databases, MLOps, LLMOps, responsible AI, data governance, security, and compliance. Provides hands-on technical leadership, evaluates emerging technologies, manages risks and dependencies, partners with stakeholders, and mentors engineering talent.

Designs and maintains scalable data pipelines, Snowflake solutions, Power BI models and dashboards, ETL/ELT processes, and analytical datasets. Performs data quality, governance, lineage, and dimensional modeling activities while partnering with stakeholders to deliver reporting and AI-enabled insights. The role also supports process automation, data architecture best practices, and team mentoring.

Designs, builds, and maintains the ASPCA’s enterprise data warehouse ecosystem. The role develops dbt Core models across medallion architecture, integrates MDM workflows, builds and operates Microsoft Fabric and Azure Data Factory pipelines, and supports Snowflake environments. Responsibilities include dimensional modeling, orchestration, monitoring, testing, troubleshooting, performance optimization, documentation, CI/CD, data governance, and quality assurance. The engineer collaborates with BI, data management, strategy, and research teams to deliver trusted, analytics-ready datasets.

Build and optimize enterprise data warehouses, data lakes, and scalable batch and streaming pipelines. Support Snowflake and Databricks ecosystems, resolve ETL/ELT bottlenecks, automate workflows with Airflow and Control-M, and manage AWS-based infrastructure. Develop production-grade Python, SQL, and shell solutions while applying DevOps, CI/CD, IaC, and data modeling practices. Lead incident management, root cause analysis, operational metrics, and Agile collaboration with data and platform teams.

Design, build, and operate batch, streaming, and hybrid data pipelines in a regulated healthcare environment. Develop dbt models in BigQuery, ingest structured and unstructured clinical data, and implement data quality, lineage, governance, and compliance controls. Support ML workflows, CI/CD, monitoring, incident response, and platform architecture. Collaborate with data scientists, clinical, quality, security, and product teams while mentoring engineers and establishing standards.

Build, optimize, and maintain batch and streaming ETL/ELT pipelines, data models, and automated workflows using AWS, Snowflake, Python, SQL, dbt, and shell scripting. Containerize and test data workloads, integrate systems, support business intelligence and API access, improve data architecture, and implement documentation and security governance. Collaborate cross-functionally on time-bound projects while developing expertise in education data and advancing CCGI’s educational equity mission.

Supports an enterprise data warehouse and analytics platform by developing data flows, models, dashboards, visualizations, automation, and actionable business insights. The role gathers stakeholder requirements, performs data cleaning and transformation, develops solutions using SQL, Python, and dbt, and builds Tableau or other BI dashboards. It also promotes analytics best practices, monitors adoption of initiatives, and collaborates with cross-functional teams and junior engineers.

Build and operate scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design data models, enforce engineering patterns and security standards, support distributed and streaming workloads, and deliver resilient solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring teammates and communicating technical concepts to stakeholders.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Develop distributed, real-time, and warehouse solutions using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Apply data security, encryption, access controls, observability, and reusable engineering patterns. Collaborate with product managers, engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include distributed data processing, data modeling, relational and NoSQL database development, data security, observability, orchestration, and data warehousing. The role collaborates with engineering, analytics, product, and data science teams; establishes reusable engineering patterns; communicates technical concepts; and mentors data professionals.

Build and operate scalable cloud data pipelines, platforms, and applications for risk management. Design data architectures, enforce engineering standards, implement security controls, and work with distributed data, AI, microservices, lakehouse systems, and relational and NoSQL databases. Collaborate across Agile teams, mentor data professionals, communicate technical outcomes, and deliver resilient solutions using technologies such as Python, Spark, Databricks, Snowflake, and cloud platforms.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, engineers, and product managers while applying distributed computing, lakehouse architecture, data modeling, security, and observability practices. Develop resilient relational and NoSQL data solutions, mentor team members, enforce engineering standards, and communicate technical outcomes to stakeholders. The role requires independent delivery of cloud-first data products supporting large-scale financial experiences.

Builds data ingestion, integration, transformation, modeling, and quality frameworks for DHS financial management programs. The role uses SQL, Python, R, PySpark, and Databricks to analyze large datasets, develop predictive and machine learning models, support data governance, and generate decision-making insights. Responsibilities also include client meetings, documentation, presentations, client service, and business development support.

Design, build, and maintain scalable batch and streaming data pipelines, cloud infrastructure, and the Enterprise Data Hub. Partner with stakeholders to productionize ingestions, ensure data quality, implement metadata/catalog solutions, mentor junior engineers, and support analytics and ML workloads using cloud and big-data tooling.

Build and maintain scalable data platforms, databases, ETL/ELT pipelines, data models, and CI/CD workflows. Translate stakeholder requirements into technical solutions, implement data governance and quality practices, integrate APIs and external sources, optimize infrastructure, and support analytics and data science teams. The role requires Python, PostgreSQL, AWS, production pipeline operations, monitoring, and eligibility for Secret clearance.

Bridges procurement stakeholders, governance teams, business analysts, and engineers to define data contracts, map procurement data to OCDS, and build Databricks pipelines. Responsibilities include requirements gathering, source-to-target mapping, data modeling, validation, quality controls, lineage, governance, and production support. Develops Bronze, Silver, and Gold data layers using Spark, PySpark, Python, SQL, Delta Lake, and Databricks services while supporting Agile delivery, testing, deployment, and troubleshooting.

Designs, develops, optimizes, and supports SQL Server databases, data warehouses, ODS platforms, ETL processes, and business intelligence solutions. Builds Power BI, SSRS, SSAS Tabular, and DAX solutions; governs data quality, lineage, access, and change management. Troubleshoots technical issues, supports multiple database platforms, collaborates with stakeholders and vendors, documents technical environments, and oversees outsourced DBA service delivery.

Build and maintain data infrastructure, software systems, ETL processes, analytics tools, and metrics for the Starlink Mobile network. Aggregate and analyze large datasets, detect application issues, fuse data from multiple sources, create data catalogs, and develop predictive models and machine learning pipelines. Collaborate with engineering teams, operators, and external partners to assess network growth and effectiveness.

Leads the design, development, optimization, and maintenance of scalable ETL pipelines and cloud-based data platforms. Builds Python data-processing applications, engineers ingestion and transformation workflows, improves data quality and reliability, and troubleshoots complex production issues. Collaborates with engineering teams and mission stakeholders, supports testing and deployment, modernizes data workflows, documents architectures, and guides best practices in an Agile environment.

Designs and optimizes scalable data pipelines, services, and models using Python and SQL. Ensures data quality, reliability, and performance across large-scale storage and processing systems. Collaborates with analysts, data scientists, engineers, and cross-functional teams to deliver actionable data solutions. Integrates LLM and agent-based capabilities into data platforms and supports clinical, business intelligence, and operational needs.

Design, build, and maintain scalable data pipelines, warehouses/lakes, and platforms. Lead multidisciplinary teams to implement ETL/ELT (batch and streaming), optimize data lake performance, enforce data governance, and support analytics and ML use cases on cloud and on-prem environments.

Build and deploy scalable data pipelines and platforms that organize structured and unstructured data. Perform ETL, pipeline integration, analytical exploration, and data platform design and maintenance in agile, multidisciplinary teams. Develop deployable software, support data visualization, and work with relational and non-relational databases. Projects support missions including fraud detection, cancer research, and national intelligence.

Build and deploy scalable data pipelines and platforms that organize structured and unstructured data for mission-driven clients. Collaborate with analysts, developers, and data consumers in agile teams while supporting data platform assessment, design, development, maintenance, analytical exploration, and data examination. Apply ETL, software development, visualization, database, automation, and cloud technologies to support applications including fraud detection, cancer research, and national intelligence.

Designs, develops, and operates scalable batch and streaming data pipelines and products. Integrates data from multiple sources, improves reliability and performance, establishes reusable engineering patterns, and supports AI, search, analytics, and machine learning workloads. Partners with technical and business stakeholders on architecture and requirements, while providing technical guidance and mentoring to less-experienced engineers.

Develops and maintains scalable data ingestion, transformation, modeling, and warehousing pipelines. Supports ETL/ELT workflows, data quality validation, governance, documentation, cloud platform deployment, and compute/storage optimization. Collaborates with analysts, data scientists, and product teams, participates in Agile ceremonies and code reviews, and helps improve engineering processes using SQL, Python, and cloud technologies.