Senior Data Engineer - AWS Lakehouse

Help shape a modern, cloud-native data platform from the ground up.

We're building a next-generation AWS-based Lakehouse and are looking for a hands-on Senior Data Engineer who thrives at the intersection of architecture and execution. In this role, you'll take high-level designs and turn them into production-ready ingestion pipelines, Iceberg tables, and data marts that power analytics and downstream data consumers at scale. This project is for a large government agency in the Washington, DC metropolitan area. This position is hybrid and must be available to work onsite as needed. This is a 1 year project.

You'll work closely with our Principal Data Architect, playing a critical role in translating architectural vision into reliable, performant systems. If you enjoy solving complex data problems, working with modern open table formats, and building platforms that handle large-scale, real-world data, this role is for you.

What You'll Do

• Design and build scalable ETL/ELT pipelines from Oracle and other source systems into an AWS Lakehouse.
• Implement row-level updates using Apache Iceberg MERGE and UPDATE patterns.
• Own the lifecycle of Iceberg tables, including partitioning, schema evolution, compaction, and snapshot management.
• Develop batch and incremental ingestion workflows, including full extracts and CDC-based pipelines.
• Create and maintain processing and data marts that support editing, imputation, and data dissemination.
• Optimize query and catalog performance across Glue Catalog, Athena, EMR, and Spark.
• Ensure strong data quality, lineage, and governance across the platform.
• Collaborate closely with the Principal Data Architect to operationalize designs and continuously improve the platform.

What We're Looking For

• 3–7 years of hands-on data engineering experience.
• Strong experience building on AWS, including S3, Glue, EMR, Athena, Lambda, and Step Functions.
• Deep, practical experience with Apache Iceberg, including:
  • Partitioning, compaction, and schema evolution
  • Row-level operations (MERGE INTO, updates, deletes)
  • Snapshot and table version management
• Advanced SQL skills and strong experience with Spark (PySpark or Scala).
• Must be AWS Certified.
• Proven ability to build and operate pipelines for large-scale (multi-TB) datasets.
• Solid understanding of batch, incremental, and CDC ingestion patterns.
• Experience implementing data quality checks and governance best practices.
• Solid communication skills. Able to work well with teams as well as independently.

Nice to Have

• Experience migrating from Oracle or other RDBMS platforms to cloud-native data architectures.
• Exposure to other Lakehouse formats such as Delta Lake or Apache Hudi.
• Familiarity with AI/ML-assisted data cleaning or imputation techniques.
• Experience working with government systems and architectures.

Why This Role?

• Build a modern Lakehouse platform using today's best-in-class open technologies.
• Work closely with senior technical leadership and have real influence on design and implementation.
• Solve meaningful data engineering challenges at scale.
• Opportunity to grow as a technical leader while remaining deeply hands-on.

Synectics is an Equal Opportunity Employer.