Top Data & Analytics Jobs

The Data Engineer designs, builds, and optimizes enterprise data pipelines, lakes, warehouses, integrations, and analytics infrastructure. Responsibilities include ELT development using Snowflake and Informatica, data governance, quality, security, performance monitoring, PII protection, cost optimization, and support for analysts and data scientists. The role also delivers process improvements, analytics tools, stakeholder collaboration, technical mentoring, and scalable data architecture across cross-functional teams.

Design and implement data pipelines, ensuring data movement, transformation, storage, availability, and scalability for AI/ML and mission applications. Collaborate with software engineers and data scientists using Databricks, AWS, ETL processes, relational databases, and Agile Scrum. Build solutions supporting defense and intelligence customers, including data-driven dashboards and deployments across JWICS, NIPR, and SIPR environments.

Design, build, and optimize modern Azure and Databricks data platforms for clients. Develop batch and streaming ingestion pipelines, lakehouse and medallion architectures, dimensional models, and transformation workflows. Lead discovery sessions, requirements workshops, architecture discussions, and solution reviews. Recommend practices for governance, lineage, monitoring, DevOps, security, and data modeling while translating business needs into scalable technical solutions and communicating with technical and executive stakeholders.

Designs, builds, and optimizes modern Azure and Databricks data platforms for clients. Responsibilities include client discovery, requirements workshops, architecture discussions, batch and streaming ingestion, lakehouse and dimensional modeling, data transformation, governance, monitoring, DevOps, security, and solution reviews. The role also communicates recommendations to technical stakeholders and leadership while delivering scalable analytics and reporting solutions.

Designs and maintains scalable data pipelines, architectures, ETL processes, data models, and governance frameworks for secure DoD analytics and AI initiatives. Integrates complex data sources, ensures data quality and compliance, and supports deployment and sustainment of data platforms. Collaborates with data scientists, product managers, and mission stakeholders to deliver operational decision-support solutions.

Designs and optimizes scalable data pipelines and architectures for Department of Defense missions, AI-enabled use cases, and operational analytics. Integrates complex data sources while ensuring quality, reliability, security, governance, and compliance. Builds ETL processes and data models, collaborates with data scientists and mission stakeholders, and supports deployment and sustainment of data platforms in secure DoD environments.

The ClaimsXM Data Engineer III role has limited responsibilities listed. The position requires U.S. citizenship or naturalized citizenship and requires all work to be completed within the continental United States, Alaska, or Hawaii.

Build and support knowledge graph and object-based intelligence capabilities for classified government customers. Responsibilities include graph data modeling, graph database development, querying with Cypher, SQL, and SPARQL, scripting in Python and shell, collaborating with software teams, and communicating technical concepts to analysts and senior stakeholders. The role requires onsite work in a SCIF and active TS/SCI clearance with willingness to obtain a CI polygraph.

Develop and deploy data pipelines and platforms that organize disparate structured and unstructured data. Responsibilities include data acquisition, preparation, database architecture, ETL development, cloud-based data and machine learning pipelines, containerization, deployment, and MLOps ecosystem development. The role collaborates with data engineers, developers, and data consumers in an agile environment and requires Top Secret clearance.

Designs and supports Databricks and Delta Lake pipelines for an enterprise data lakehouse. Develops Python and SQL transformations, data models, quality checks, batch and streaming pipelines, API integrations, and monitoring processes. Partners with technical and business stakeholders to translate data needs into maintainable solutions and sprint work. Troubleshoots performance and data-quality issues, performs testing, documents reusable engineering patterns, and supports production operations across cloud-based data platforms.

Design, develop, and support Denodo virtual views and data services; integrate Denodo with databases, APIs and cloud platforms; build reusable semantic/logical layers; optimize queries and performance; collaborate with architects and business teams; document designs, test, review code, deploy, and provide production support.

Migrate data products from Azure Synapse to Microsoft Fabric and design scalable data pipelines using Azure Data Factory, notebooks, Spark/PySpark, Python, and SQL. Implement ingestion (full, delta, CDC), refactor pandas to PySpark, apply medallion architecture, perform data quality checks, tuning, monitoring, troubleshooting, and collaborate with stakeholders to document and deploy solutions.

Own and improve ETL pipelines, investigate and validate analytics systems, optimize performance, cost, and availability, and design scalable data platforms. Collaborate with data scientists to deliver dashboards, insights, and client-facing data pipelines using strong software engineering practices.

Design and build end-to-end data pipelines and integrations on Databricks, implement medallion architectures, develop streaming and NiFi pipelines, integrate purpose-built stores, operationalize ML/MLOps (training, deployment, monitoring), and explore LLM/RAG generative AI solutions while enforcing data governance, security, and performance for large-scale analytics.

The Data Engineer will clean, manage, and analyze datasets, automate ETL processes, and collaborate with traders and software engineers to derive insights.

Design and maintain petabyte-scale data architectures and ETL pipelines; enable automated MLOps workflows; lead teams building ML systems and backend services; implement CI/CD and DevSecOps practices; integrate components, write maintainable Python code, and produce technical documentation while collaborating with cross-functional teams.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

The Data Engineer designs and maintains data pipelines for advanced analytics in supply chain, ensuring data quality and reliability while collaborating with cross-functional teams.

Design, build, and optimize end-to-end data pipelines, ETL processes, and data models to support purchasing and finance analytics. Collaborate with data scientists and stakeholders to deploy predictive models, ensure data quality and governance, and deliver dashboards and analytics for operational insight and forecasting.

The Data Engineer will analyze data, optimize systems, and collaborate with data scientists and engineers, requiring strong SQL and PowerBI skills.

Build and own production data pipelines and financial data models to turn payments and operational data into trusted metrics and actionable insights. Ship features for Whop Financial Reports, standardize financial semantics, ensure data quality and lineage, and enable self-serve analytics across Finance, Product, GTM, and Exec teams.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Design, build, and maintain scalable data pipelines and ETL/ELT processes. Integrate data from multiple sources, design and manage databases/data warehouses, ensure data quality and governance, automate workflows, maintain compliance (e.g., GDPR, HIPAA), and optimize data system performance in collaboration with product and engineering teams.

Build and maintain end-to-end event-driven analytics pipelines for telemetry from robots and cloud systems. Own metrics catalog, schema evolution, data quality validation, and performant analytical models and queries to enable reporting and self-service analytics.