Lead AWS Data Engineer

Must-Have: Experience Candidates should demonstrate 7+ years of data engineering experience with depth in:

• Cloud data warehouse / lakehouse — Amazon Redshift or Databricks. Real production ownership: modeling, performance tuning, cost management, workload isolation.
• PythonApache Airflow — authoring and operating DAGs at scale; dependency management, retries, SLAs, observability.dbt — model design, testing, macros, documentation, CI/CD for transformations.
• Data movement / ingestion tooling — e.g. Fivetran, Airbyte, AWS DMS, Kafka/Kinesis, or custom CDC and API ingestion frameworks.
• Open table formats — Apache Iceberg / Delta Lake / Hudi. Partitioning, schema evolution, compaction, timetravel.
• Apache Spark — PySpark or Spark SQL for large-scale batch/streaming transformation.
• SQL — advanced, including window functions and query optimization.
• Data Governance &Data quality & observability tooling (Great Expectations, Monte Carlo, Elementary)
• Nice-to-HaveAWS ecosystem depth (S3, Glue, Lambda, IAM, EMR)
• Streaming / near-real-time pipelines
• Infrastructure-as-code (Terraform), Docker, CI/CD (GitHub Actions