Top Data Engineer Jobs

The Data Engineer will maintain and optimize data pipelines, manage infrastructure, support ML workflows, and collaborate with teams on data products.

Design, develop, test, maintain, and support secure, scalable data solutions using Snowflake and Databricks. Lead technical design, author specifications, review code, mentor engineers, ensure SDLC, audit/security compliance, and drive technical strategy while collaborating with architects and business partners. Participate in Agile ceremonies, be on-call as needed, and travel occasionally.

As an Analytics Engineer, you'll manage the data stack, design database architecture, build data pipelines, and oversee reporting and data quality.

Design, build, and maintain batch and real-time ETL pipelines and data lake architecture using AWS services, Python, PySpark, and SQL. Ensure data quality, governance, and compliance; support reporting and ML models; mentor junior engineers and document data products.

Design, build, and maintain scalable data ingestion and ETL pipelines across a growing data lake. Define and enforce data quality standards, SLOs, and validation frameworks. Optimize pipelines for performance and cost, expand monitoring and alerting, and enable stakeholders to self-serve on data infrastructure.

Lead design, build, and deployment of secure, scalable data pipelines and analytics platforms (including Palantir) in constrained or classified environments. Mentor multidisciplinary teams, integrate AI/ML and LLM capabilities into production workflows, implement DevSecOps practices, and communicate architectures to technical and nontechnical stakeholders.

Design, build, and maintain enterprise-scale data platforms and ETL/ELT pipelines. Develop software and scripts to ingest, parse, and process structured and unstructured data. Collaborate with cross-functional teams to scale, monitor, and operate data solutions using Databricks, Spark, and CI/CD practices to support mission-driven analytics.

Design, build, and maintain scalable cloud data platforms and ELT pipelines using AWS, Snowflake, and dbt. Implement data models, orchestration, IaC, data quality, monitoring, and CI/CD. Support production operations, compliance, and mentor junior teammates.

Build and maintain Azure Data Factory ingestion pipelines into Snowflake, implement medallion transformations (Coalesce), write Python orchestration and connectors, apply governance with Atlan, collaborate with AI coding agents, participate in CI/CD with Azure DevOps, handle on-call production support, and document runbooks.

Design, build, and maintain data transfers and databases. Produce efficient, reusable data engineering code, perform data modeling, UAT, data cleaning, reporting, and technical documentation. Collaborate with stakeholders, participate in code reviews, and improve automation and processes to meet business requirements.

Build and maintain data infrastructure to measure agent behavior and user interactions, design evaluation frameworks for AI agents, analyze user behavior to inform product strategy, and partner with engineering and leadership to solve high-impact business problems using data.

Design, build, and maintain ETL/ELT pipelines and enterprise data products. Monitor data pipelines, support data modeling and semantic layers, enforce governance, and collaborate with analytics and AI teams to deliver context windows and embeddings for AI/ML workflows.

Lead and mentor a data engineering team, define technical vision and strategy, oversee data pipeline design, and collaborate with cross-functional teams.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

Lead design and delivery of complex data solutions including data integration, cleaning, transformation, lineage, and migration. Build and document data pipelines, manage ADS and regulatory data lineage, lead testing and cutover for database migrations, create automation and reconciliation scripts, troubleshoot with architects and security teams, and mentor other data engineers.

The Data Engineer will execute data architecture projects, manage data ingestion processes, ensure data quality, and transition data to new systems while collaborating with investment teams.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

Lead architecture and delivery of cloud data platforms, design scalable data warehouse and lake solutions, implement ETL/ELT frameworks, optimize platform performance and resilience, champion automation, governance, and mentor engineering teams to deliver enterprise analytics and data products.

Design, build, and maintain production-grade ETL/ELT pipelines and data models using dbt, Spark, and cloud platforms. Manage ingestion, data quality, observability, semantic layers, and BI outputs. Collaborate with analysts and data scientists to deliver governed, high-quality data products and implement DataOps and CI/CD practices.

Design, build, and maintain AWS-integrated data pipelines and infrastructure. Translate business needs into schemas and data dictionaries, optimize for scalability, ensure data governance, troubleshoot stakeholders' issues, and provide Tier 3 support for deployed applications and dataflows.

Design, build, and maintain enterprise analytics solutions, semantic models, and reporting datasets using Microsoft Fabric, Databricks, SQL, Python, and BI tools. Collaborate with product, platform, and business teams to ensure data quality, performance, scalability, and metric governance. Mentor engineers, lead data design discussions, and drive analytics engineering best practices to enable self-service analytics and executive reporting.

Design and implement large-scale GCP-based data platforms and ETL using PySpark/Spark, BigQuery, Dataproc, Composer/Airflow. Process and analyze large datasets, write SQL, collaborate on BI delivery, and work in a mature Agile team.

Build and maintain scalable cloud data pipelines and governance to support analytics across claims, EHR, and real-time patient data. Deploy and operate Snowflake/dbt/AWS-based infrastructure, enable real-time analytics, partner with clinical, product, and engineering teams, and deliver insights that inform care delivery, payer collaboration, and business decisions.

Build and maintain data pipelines and models to provide high-quality data for reporting, BI, and analytics. Work with SQL, dbt, Python, cloud data stores, orchestration tools, and reconciliation systems to support analytics, BI tools, and data warehouse delivery.

Design, build, and maintain ETL/data pipelines and data models (dbt). Work with large datasets across RDBMS and NoSQL, deploy to cloud (AWS preferred), use Python and orchestration tools (Dagster/Prefect/Airflow), support BI/analytics and visualization, and implement data reconciliation for accuracy.