Top Senior Data Engineer Jobs

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Technical lead for data engineering who designs, builds, and owns scalable Snowflake ELT pipelines and data models. Mentors engineers, drives standards, optimizes performance and costs, and partners with product, analytics, QA, and governance to deliver observable, high-quality data solutions. Contributes to platform strategy, DataOps/CI/CD adoption, and ensures data quality, testing, and documentation.

Design, architect, and build scalable data platforms and pipelines (Databricks on AWS). Implement ETL and API data interfaces, optimize data infrastructure and streaming (Kafka), and improve data quality and availability. Collaborate in an agile scrum team supporting a high-visibility public-facing product. Must be a U.S. citizen eligible for Public Trust clearance.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.

The Senior Data Engineer will manage data tuning on relational databases, utilize Big Data technologies, and coordinate with technical teams and business users.

Lead the analytics data layer: design and operate a ClickHouse-backed warehouse, define and validate trusted metrics, build customer-facing React dashboards and internal Grafana/self-serve tooling, and ensure data quality, lineage, and documentation for telemetry-derived insights.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.