Top Data & Analytics Jobs

Designs, develops, and maintains ETL/ELT pipelines, APIs, and data integration services for analytics, reporting, and operational needs. Responsibilities include data mapping, transformation, validation, ingestion automation, performance optimization, cloud architecture support, metadata and lineage implementation, workflow documentation, and collaboration with data governance and security teams. The role also supports source analysis, requirements gathering, interface design, testing, deployment, and enterprise data catalog initiatives.

Own end-to-end data platform architecture across ingestion, transformation, modeling, and serving. Improve warehouse efficiency, access control, reliability, observability, orchestration, CI/CD, and developer experience. Lead cross-functional technical initiatives, build monitoring and alerting, mentor engineers, guide architectural decisions, and develop AI-powered data tooling. The role requires strong SQL and Python skills, production experience with Airflow, BigQuery, and containerized infrastructure, plus architectural ownership and stakeholder communication.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architecture. Partner with cross-functional teams to define KPIs, ensure data quality and governance, own billing data infrastructure, and deliver dashboards and analytics for leadership while mentoring peers.

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.

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.

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.

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

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.

The Senior IT Data Engineer will design and implement enterprise data architectures, develop scalable ETL/ELT pipelines, and support data-driven initiatives for the Federal Reserve Board.

Design, build, and maintain scalable ETL pipelines using Python, SQL, and Palantir or similar platforms. Ingest structured and unstructured data from databases, APIs, and files; implement data models, cleansing, validation, monitoring, and performance optimizations; collaborate with stakeholders and document pipelines and best practices.

The Principal Data Engineer will design and implement scalable data platforms, lead architecture discussions, and mentor teams. Requires expertise in Azure and Databricks.

Design and implement scalable, secure enterprise geospatial database solutions using Esri technology. Collaborate across project phases to configure, maintain, tune, secure, and automate databases, support ArcGIS applications, and provide technical guidance for migrations, integrations, and operational readiness for Defense/Intel clients.

Consult with customers to design and implement enterprise geospatial database solutions using Esri/ArcGIS technologies. Configure, secure, monitor, and automate databases (backup/HA/DR, performance tuning, data loading). Collaborate across teams, provide technical guidance, and support database migrations and integrations for mission-critical defense and intel applications.

Consult with customers to design and implement scalable, secure enterprise geospatial database solutions using Esri/ArcGIS technologies. Configure and maintain database environments (backup, HA/DR), monitor and tune performance, implement security and access controls, automate data workflows, and provide technical guidance across requirements, design, build, and deployment phases. Support integration with ArcGIS web, desktop, and mobile applications for Defense and Intel clients.

The Data Engineer will manage and develop data warehousing solutions, implement ETL processes, and utilize SQL for data analysis and reporting. Experience in analytics tools and communication is essential.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

Build, maintain, and optimize ETL/ELT data pipelines using Databricks, Apache Spark, and SQL. Support data ingestion, transformation, SSIS integration, and SQL Server development. Troubleshoot data quality, assist production support, document processes, and collaborate with analysts and stakeholders. Support data governance, HIPAA compliance, and healthcare data initiatives.

The role involves designing and scaling data infrastructures for private equity, developing ETL/ELT pipelines, ensuring data quality, and collaborating with various teams on data governance.

The Data Engineer will develop secure, scalable data pipelines for IRS applications, supporting complex analytics and machine learning to combat fraud and ensure compliance. Responsibilities include pipeline engineering, troubleshooting, and collaboration with data teams to create production-ready analytics solutions.