Insurance Data Engineering - Data Engineer

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Skills you'll use

What you'll do

• Design, build, and maintain scalable, fault-tolerant data pipelines and ETL/ELT processes for structured, semi-structured, and unstructured data.
• Own end-to-end pipeline orchestration - scheduling, dependency management, retries, SLAs, and observability - using a data pipeline orchestrator.
• Build and manage lakehouse data assets on Apache Iceberg, including partitioning, schema evolution, time-travel, and table maintenance (compaction, snapshot expiry).
• Develop and operate large-scale data processing jobs using Apache Spark, including AWS Glue Spark jobs.
• Model, load, and optimize data in Snowflake, and govern data assets using data catalogs (AWS Glue Data Catalog and Snowflake Horizon).
• Architect and implement data solutions on AWS using native services (S3, Glue, EMR, Lambda, Athena, Kinesis, Redshift, IAM).
• Ensure data quality, integrity, lineage, and security across all systems and environments.
• Integrate data from diverse sources including APIs, relational databases, streaming feeds, and third-party tools.
• Monitor, troubleshoot, and tune pipelines for performance, reliability, and cost efficiency.
• Contribute to platform architecture, design reviews, code reviews, and engineering best practices.

Who can apply

• 5-8 years of hands-on data engineering experience building production data pipelines.
• Strong proficiency in Python for data engineering and automation.
• Advanced SQL and strong experience with relational databases (e.g., PostgreSQL, MySQL).
• A data pipeline orchestrator is mandatory - proven experience operating a workflow orchestration tool (e.g., Apache Airflow, Dagster, or equivalent) in production.
• Hands-on experience with Apache Spark for large-scale distributed data processing.
• Production experience with Apache Iceberg (or an equivalent open table format) for lakehouse storage.
• Hands-on experience with AWS Glue (ETL jobs and the Glue Data Catalog).
• Experience with Snowflake as a cloud data warehouse.
• Experience with data cataloging and governance using AWS Glue Data Catalog and Snowflake Horizon.
• Strong experience with AWS as the primary cloud platform, including S3, EMR, Lambda, Athena, Kinesis, Redshift, and IAM.

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