Principal Data Engineer

We are looking for a Lead Data Engineer to be the technical anchor for a squad of junior and mid-level data engineers working on our AWS Lakehouse. You will work within a larger initiative team, alongside senior leaders, architects, and domain experts, owning the day-to-day engineering execution for your squad's workstream. What You'll Do Hands-On Engineering • Be an active, high-output contributor: building AWS Glue pipelines, writing transformation logic, implementing data models, and debugging complex issues. • Implement harmonization and modeling workstreams using open table formats (Apache Iceberg or Delta Lake), ensuring correct partitioning, schema evolution, and data quality patterns. • Build and optimize consumption layer pipelines serving analytical workloads via Athena. • Apply engineering best practices across your work: testing, documentation, code modularity, and performance awareness. Technical Guidance for the Squad • Serve as the go-to technical resource for junior engineers — pair programming, answering implementation questions, and reviewing code with a teaching mindset. • Conduct thorough, constructive code reviews that raise the quality bar and accelerate learning. • Flag technical risks or blockers to initiative leads and architects early, with context and proposed options. Delivery & Collaboration • Translate workstream requirements — defined by initiative leads — into well-scoped engineering tasks for the squad. • Keep delivery on track at the squad level: identify dependencies, surface blockers, and coordinate with peers across the broader initiative team. • Participate actively in technical discussions, contributing implementation-level insight to broader initiative planning. Required • 5+ years of data engineering experience, with a track record of delivering complex pipelines and data models in production. • Strong, hands-on experience with AWS Glue — authoring, scheduling, and troubleshooting ETL jobs at scale. • Deep working knowledge of core AWS data services: S3, Athena, and the broader AWS data ecosystem. • Practical experience with open table formats — Apache Iceberg or Delta Lake — including partitioning, schema evolution, and compaction. • Grounding in data modeling concepts: dimensional modeling or similar approaches applied in a lakehouse or data warehouse context. • Experience informally leading or mentoring junior engineers — through code review, pairing, or task guidance. • Strong problem-solving instincts: able to work through implementation complexity independently and know when to escalate. • Clear communicator: comfortable asking good questions, giving precise technical feedback, and flagging issues early. Preferred • Familiarity with AI-assisted development tools (e.g., GitHub Copilot, Amazon CodeWhisperer, or similar) and a genuine openness to integrating them into day-to-day engineering workflows. • Experience with Apache Spark (via AWS Glue or EMR) for large-scale data processing. • Familiarity with streaming data patterns (Flink, Kafka, Kinesis) and how they integrate with lakehouse architectures. • Exposure to CI/CD practices for data pipelines. • AWS certifications (e.g., Data Engineer – Associate, Solutions Architect).