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Strong employer brand, popular Data Engineer title, and broad common stack drive high candidate competition.
Skills are broadly transferable across industries despite optional financial services preference.
Explicit 8+ years plus mandatory platform, orchestration, and cloud/warehouse experience enforces strict shortlisting.
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Design, build, and maintain scalable, fault-tolerant data pipelines and orchestration using modern lakehouse technologies on AWS.
Own end-to-end development, operation, and governance of data platforms including Apache Iceberg, AWS Glue, Snowflake, and related services.
Collaborate with data scientists, analysts, and platform teams to deliver reliable, governed, and cost-efficient data products powering insurance systems analytics.
8+ years of hands-on data engineering experience building production data pipelines.
Proficiency in Python, advanced SQL, and experience with relational databases (e.g., PostgreSQL, MySQL).
Mandatory experience with a data pipeline orchestrator (e.g., Apache Airflow, Dagster) in production and with Apache Spark for large-scale data processing.
Experience with Apache Iceberg or equivalent lakehouse storage, AWS Glue (ETL jobs and Data Catalog), and Snowflake cloud data warehouse; strong AWS cloud platform skills (S3, EMR, Lambda, Athena, Kinesis, Redshift, IAM).
Experienced in operating and optimizing distributed data processing frameworks within cloud environments (AWS) and modern data lakehouse architectures.
Demonstrates ownership of end-to-end data pipeline lifecycle including orchestration, data modeling, governance, and security in production.
Skilled in cross-functional collaboration with data science and analytics teams, capable of mentoring junior engineers and driving best engineering practices.