Data Engineer Intern/Co-op

Job summary

Data Engineer Intern/Co-op At Plymouth Rock, a fast-growing, analytics-driven insurer, we believe data science can redefine how insurance operates. As a Data Engineer inter/Co-op with our Enterprise Advanced Analytics team, you will work with a team of top-tier data scientists to generate breakthrough insights that drive profitable growth, operational excellence, and competitive advantage.

Benefits

Qualifications

• Currently pursuing or have completed a Master's in Computer Science, Information Systems, Data Engineering (or related).
• Experience (projects, internships, or coursework) building Python + SQL data pipelines or data-intensive workflows.
• Familiarity with AWS services commonly used in data platforms, including S3, Glue, Step Functions (hands-on experience preferred).
• Experience working with SQL Server (or similar relational databases) and writing performant SQL.
• Comfort with Linux and bash; able to debug jobs and inspect logs.
• Familiarity with CI/CD, Git, and basic testing practices.
• Exposure to hybrid connectivity patterns (e.g., VPN/Direct Connect, hosted agents, secure credential management) or

Responsibilities

• Assist in modernizing legacy data pipelines into Python-based, cloud-native workflows using AWS Step Functions, AWS Glue, and S3.
• Build and improve ingestion pipelines that move data from SQL Server and other on-prem + cloud sources into AWS (e.g., S3-backed data lake).
• Improve pipeline performance, efficiency, and scalability (e.g., partitioning strategies, incremental loads, reduced runtime/cost).
• Implement data quality checks and operational safeguards to support reliable, repeatable (idempotent) runs (e.g., validation rules, anomaly checks, retry-safe processing).
• Contribute to reusable Python modules for ingestion, transformations, quality checks, logging, and alerting.
• Partner with data scientists/ML engineers to prepare curated datasets for SageMaker training/inference workflows.
• Document code, workflows, and runbooks; keep documentation current as pipelines evolve.

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