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Tier-1 brand, mid-level data engineer role, metro location, and popular generalist skills increase applicant competition.
Transferable data engineering skills, but insurance/finance domain preference increases relevance sensitivity.
Mandatory 5-8 years and many specific technologies create strict filtering.
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Own the end-to-end design, build, and operation of scalable, fault-tolerant data pipelines and lakehouse data assets using AWS (Glue, S3, EMR), Apache Iceberg, Spark, and Snowflake.
Ensure data quality, governance, security, and cost efficiency while partnering with data scientists, analysts, and platform teams to enable analytics and business decisions within Insurance Systems.
Lead pipeline orchestration including scheduling, dependency management, retries, and observability; mentor junior engineers and contribute to platform architecture and engineering standards.
5 to 8 years of hands-on data engineering experience building production data pipelines.
Mandatory experience with data pipeline orchestration tools (e.g., Apache Airflow, Dagster) in production.
Strong proficiency in Python and advanced SQL; hands-on experience with Apache Spark, Apache Iceberg, AWS Glue (ETL and Data Catalog), and Snowflake.
Production experience with AWS cloud services including S3, Glue, EMR, Lambda, Athena, Kinesis, Redshift, and IAM.
Experienced operating in modern data engineering environments focused on cloud-native, scalable lakehouse architectures using AWS and open table formats like Apache Iceberg.
Familiar with complex ETL/ELT workflows, data governance, orchestration, and monitoring to deliver reliable, cost-efficient data products aligned to business needs.
Comfortable working cross-functionally with data scientists and platform teams and able to mentor others while influencing platform design and engineering best practices.