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Popular mid-level data engineer role with broad AWS/Spark/dbt requirements at a recognized startup increases applicant competition.
Core data engineering skills are highly transferable across industries, reducing background sensitivity.
Explicit 4–5 year requirement plus mandatory Spark, AWS, dbt, and Airflow skills make shortlisting strict.
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Build and optimize scalable data pipelines and analytical datasets on an AWS-based lakehouse platform using technologies like Apache Iceberg, AWS Glue, Athena, dbt, and Airflow.
Maintain and develop batch data pipelines including reusable ingestion and transformation workflows, supporting schema evolution and partition optimization.
Optimize query performance in Athena, implement data validation and monitoring, and support troubleshooting in production environments.
4–5 years of experience in data engineering.
Strong hands-on experience with Python, SQL, PySpark/Spark.
Experience with AWS S3, Glue, Athena; DBT and Airflow (or equivalent); familiarity with Apache Iceberg or similar frameworks.
Understanding of partitioning and Parquet file optimization relevant to data pipelines.
Experienced with end-to-end data engineering on AWS cloud environments focused on large scale analytical datasets.
Capable of collaborating across engineering and analytics teams to deliver production-grade, reliable data solutions.
Skilled in optimizing data workflows and query performance, with a focus on maintainability and monitoring for production systems.