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 s 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.

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.

• 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.

• 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.

• 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.

• 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.