Lead AWS Data Engineer

Required: 

• 8-10 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).