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Strong brand, popular Data Engineer title, and mid-level experience make applicant competition high.
Core data engineering skills transfer across industries, though finance/insurance domain experience is preferred.
Explicit 5-8 years plus mandatory Spark, orchestration, Iceberg, Snowflake, and AWS requirements make filters strict.
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Design, build, and maintain scalable, fault-tolerant data pipelines and ELT processes using a modern lakehouse stack on AWS, Apache Iceberg, and Snowflake.
Own end-to-end data pipeline orchestration including scheduling, dependency management, retries, SLAs, and observability.
Collaborate with data scientists, analysts, and platform teams to deliver well-governed, cost-efficient data products powering analytics in Insurance Systems.
5 to 8 years of hands-on data engineering experience building production-grade pipelines.
Strong proficiency in Python and SQL; experience with relational databases like PostgreSQL or MySQL.
Experience operating data pipeline orchestrators (e.g., Apache Airflow, Dagster) in production is mandatory.
Hands-on experience with Apache Spark, Apache Iceberg, AWS Glue (including Data Catalog), Snowflake, and AWS cloud services (S3, EMR, Lambda, Athena, Kinesis, Redshift, IAM).
Experienced in end-to-end data lakehouse architecture and governance, including data modeling and cataloging.
Skilled at building large-scale distributed data processing pipelines in cloud environments with a focus on reliability and cost efficiency.
Capable of mentoring junior engineers and driving engineering best practices in a fast-paced, cross-functional, technology-driven investment firm environment.