Data Engineer

 7-10 years of professional experience in data engineering.
• Minimum 4 years building and operating production data pipelines on AWS.
• Strong programming ability in Python or Scala, with production-quality code standards.
• Expert-level SQL, including window functions, CTEs, and query plan analysis.
• Deep, hands-on Apache Spark experience — not just usage, but performance tuning and debugging.
• Demonstrated ability to diagnose Spark data skew, shuffle bottlenecks, and memory issues.
• Production experience across the AWS data stack: EMR, S3, Athena, Redshift, and Lambda.
• Streaming data experience with Amazon Kinesis or Apache Kafka.
• Workflow orchestration with Apache Airflow (MWAA) or an equivalent scheduler.
• Solid data modelling fundamentals, including dimensional modelling and slowly changing dimensions.
• Working knowledge of data lake table formats such as Apache Iceberg, Hudi, or Delta Lake.
• Experience operating what you build — CloudWatch, structured logging, ing, and on-call participation.
• Familiarity with version control and collaborative development workflows using Git.
• Ability to work directly with non-technical stakeholders and communicate trade-offs clearly.