Top Data Engineer Jobs

Manage, monitor, and optimize enterprise ETL/ELT data integration pipelines and platforms. Administer integration environments, implement observability and alerting, triage incidents (including 24/7 rotation), automate operations, maintain runbooks, and collaborate with cross-functional teams to ensure reliable data flow into the lakehouse and downstream applications.

Design, build, and scale cloud lakehouse data products (Databricks/Snowflake) from ingestion to analytics-ready datasets using medallion architecture. Develop reusable Python frameworks, Spark pipelines, and dbt/SQL transformations. Implement data modeling, governance, observability, validation, and SLA monitoring. Troubleshoot production issues, adopt AI to accelerate engineering, and provide technical leadership, code reviews, architecture decisions, and mentoring for global engineering teams.

Lead design and execution of data migration, archival, and disaster recovery for a cloud-based court case management modernization. Own migration strategy, data lifecycle and retention, DR/HA design and drills, tooling evaluation, documentation, and coordination with Data Engineering, Governance, Security, and stakeholders to ensure auditable, resilient, and compliant migrations.

Design, build, test, and maintain high-quality data pipelines and storage systems. Collaborate with architects, analysts, scientists to enable data ingestion, transformation, real-time analytics, and data reliability. Write efficient code, unit/integration tests, and apply DevOps/SDLC practices while ensuring data security and cloud infrastructure best practices.

Lead design and delivery of scalable data platforms and pipelines on Google Cloud. Build and review Python/SQL code, drive data architecture using BigQuery and GCP tooling, own ingestion/transformation/orchestration, lead client technical discussions, prototype solutions, mentor engineers, and implement IaC, CI/CD, observability, and governance.

Lead design, build, and operate a secure, scalable data platform to support analytics, MLOps, and BI. Architect data warehousing (Databricks, dbt, Spark), build ETL pipelines, optimize cloud storage and processing in GCP/AWS, implement LLM infrastructure and serving, create observability and BI dashboards, and ensure data governance and regulatory compliance (HIPAA/SOC-2). Collaborate with Data Science, ML, App, and DevOps teams.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.

Design, build, and operate production-grade realtime and batch data pipelines and internal data products. Ensure data correctness, tooling for backfills and reports, and partner with product/business teams to prioritize data acquisition and improvements. Work across query engines and streaming frameworks to support analytics, monitoring, and debugging for product and business stakeholders.

Design, build, and maintain scalable Python-based ETL/ELT data pipelines and Airflow workflows implementing Medallion (Bronze/Silver/Gold) Lakehouse patterns. Ensure data quality, performance tuning, monitoring, CI/CD, and cross-team collaboration to support analytics, reporting, and ML use cases while adhering to governance and security standards.

Technical leader for Data Engineering responsible for designing, operating, and optimizing Snowflake-based data platforms, self-hosted orchestration (Prefect), dbt practices, AWS infrastructure, and data pipelines. Drive architecture, mentor engineers, partner cross-functionally, ensure observability and production-grade practices, and enable model-ready datasets and AI/LLM workflows.

Build, monitor, and maintain data ingestion pipelines and databases (Databricks, Snowflake, SQL Server). Automate collection and monitoring with scripts, administer managed file transfers, create CI/CD and monitoring/alerting, diagnose incidents, and document/runbook operational procedures.

Design, build, and operate controlled ETL/ELT pipelines and reconciliations between Delphi, ServiceNow, and PRISM. Maintain common-key mappings, enforce data-quality assertions, implement row/column access controls, manage exception queues, and produce documentation and runbooks enabling government staff to reproduce results independently.

Design, build, optimize, and modernize enterprise Microsoft data platforms (Azure, Fabric). Develop ETL/ELT pipelines, data warehouses/lakehouses, implement governance, security, DevOps/CI-CD, and support Power BI and AI initiatives while consulting with stakeholders.

Design, develop, and maintain large-scale data pipelines and ETL processes. Implement and optimize data storage and processing on BigQuery/GCP, ensure data quality and monitoring, participate in code reviews, mentor junior engineers, and help launch data-driven tools.

Design, build, and maintain data ingestion pipelines and integrations from intermittently-connected robots to analytics, ML training, and operations. Implement SDKs, connectors, schema contracts, retries, backfills, monitoring, and optimize pipeline performance, scalability, and cost. Collaborate with robotics, autonomy, and deployment teams to ensure reliable end-to-end data delivery and quality.

Design and build a scalable data platform and tooling for robotics/autonomy data ingestion, integration, and delivery. Build SDKs, connectors, and APIs (REST/gRPC, webhooks, CDC) to integrate diverse sources and downstream ML/BI systems, ensure schema/versioning/reliability, and optimize pipeline performance, scalability, and cost.

As a Senior Staff Data Engineer, you'll set the technical vision for data architecture, lead initiatives across teams, mentor engineers, and ensure platform reliability, while integrating data systems with ML pipelines effectively.

Lead the design and optimization of enterprise-scale data warehousing and analytics solutions, utilizing Snowflake and advanced SQL for global business operations and decision-making.

Design, build, and optimize scalable data ingestion and transformation pipelines in a medallion (Bronze/Silver/Gold) architecture on Microsoft Fabric/OneLake. Ensure HIPAA-compliant, multi-tenant data isolation, data quality monitoring, lineage and governance. Partner with Data Science, ML Engineering, and Software Engineering to deliver feature stores, ML-ready datasets, and production-grade data delivery for analytics and scoring.

Lead end-to-end analytics solutions: gather requirements with stakeholders, build production-quality ETL and visualizations, coach junior analysts, communicate progress and roadblocks, support proposals and pitches, and contribute to firm initiatives and client delivery.

Design, build, and maintain scalable data warehouses, marts, and BI reporting; create robust data models and pipelines; perform audits and data quality tests; collaborate with cross-functional teams to provide single-source-of-truth analytics and support business decision-making. Requires active security clearance.

Lead data engineering teams to design, build, and maintain scalable cloud-native data pipelines, data platforms, and data products using Snowflake, Databricks, Airflow, and modern architectures. Optimize ETL/ELT, SQL, and transformations for analytics, reporting, and AI use cases; ensure data quality, governance, observability, and stakeholder alignment. Produce documentation, runbooks, and present technical solutions to business and technical audiences.

Design and lead data lake architecture and Databricks platform solutions for DoW projects. Develop and optimize ETL/Delta Lake pipelines, integrate AI/ML workflows, implement DevSecOps/CI-CD, ensure cloud security and compliance across AWS/Azure/GCP, and liaise between clients and developers to deliver solutions.

Design, build, and maintain scalable, secure cloud data pipelines and integrations. Develop APIs and modular components, apply generative AI to pipelines, enforce data governance and compliance for PHI/PII/PCI, mentor engineers, collaborate with analysts and data scientists, and ensure production reliability using containerized deployments and DevSecOps/Agile practices.

Design, build, and operate a Snowflake-centered cloud data platform: ingest with Fivetran, transform with dbt, orchestrate with Airflow/Astronomer, ensure observability and data quality, deliver CI/CD workflows, troubleshoot pipelines, guide Cortex Code adoption, and mentor junior engineers while partnering with cross-functional teams.