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Remote role, generalist mid-senior data position, metro location, and broad AWS/data stack requirements increase competition.
Core data engineering skills are widely transferable across industries despite insurance domain context.
Explicit 7–12 years requirement plus many mandatory AWS and data stack skills increases shortlisting strictness.
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Design, implement, and maintain scalable data pipelines and infrastructure on AWS to support big data processing and analytics.
Optimize performance, scalability, and reliability of data processing jobs using distributed computing platforms like Spark and Kafka.
Lead and mentor junior data engineers and collaborate cross-functionally to develop data tools and platforms enabling advanced analytics and reporting.
7 to 12 years of experience as a Data Engineer or in a similar role with strong AWS expertise.
Hands-on experience with AWS services such as S3, DMS, Lambda, EMR, Glue, Redshift, RDS (Postgres), Athena, Kinesis.
Proficiency in Python, PySpark, SQL/PLSQL for data pipelines and ETL processes.
Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
Experienced in enterprise cloud data architecture, especially using AWS Cloud and modern data stack technologies.
Technical leader capable of mentoring teams and managing performance, cost, and delivery of data engineering solutions.
Familiar with DevOps CI/CD practices for data pipelines and advanced data governance concepts like data observability and metadata management.