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Strong Tier-1 brand, metro location, and mid-level generalist data role increase candidate competition.
Core data engineering skills are transferable across industries, though insurance domain experience is beneficial.
Multiple mandatory technical requirements and explicit 5-8 years experience make shortlisting highly strict.
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Design, build, and maintain scalable, fault-tolerant production data pipelines and ETL/ELT processes on AWS for Insurance Systems.
Own end-to-end data pipeline lifecycle including ingestion, orchestration, transformation, storage, governance, and monitoring using Apache Iceberg, AWS Glue, Snowflake and data pipeline orchestrators.
Collaborate with data scientists, analysts, and platform teams to deliver cost-efficient, well-governed data products supporting analytics and business decisions.
5-8 years of hands-on data engineering experience building production data pipelines.
Strong proficiency in Python and advanced SQL; experience with relational databases (e.g., PostgreSQL, MySQL).
Mandatory experience with data pipeline orchestration tools in production (e.g., Apache Airflow, Dagster).
Hands-on experience with AWS services (S3, Glue, EMR, Lambda, Athena, Kinesis, Redshift, IAM), Apache Spark, Apache Iceberg, and Snowflake.
Experienced in modern lakehouse architectures and cloud-native data engineering on AWS, with deep knowledge of Apache Iceberg and Snowflake ecosystems.
Skilled in managing end-to-end pipeline orchestration and operational excellence in data workflow automation.
Proven ability to collaborate cross-functionally with data scientists, analysts, and platform teams and mentor junior engineers in a fast-paced agile environment.