Data Engineer

Overview As Data Engineer on the Enterprise Advanced Analytics team, you design and manage data pipelines that connect internal and external sources to advanced analytics platforms. You’ll enable top-tier data scientists to generate insights driving growth, operational excellence, and competitive advantage. You’ll shape cloud and ML infrastructure, collaborate across IT, Data Science, and business units, and lead hands-on, scalable data engineering initiatives that power enterprise-wide innovation. Compensation / Benefits 4 weeks accrued paid time off 10 paid national holidays Health insurance from Day 1401(k) employer contribution up to 7.5%Tuition reimbursement Paid parental leave Responsibilities Design, build, and optimize data pipelines and workflows for advanced research and analytics Collaborate with Data Science, IT, and Business to automate data and ML processes across Personal Auto and Homeowner lines Develop and enforce data governance and security protocols for data quality and regulatory compliance Transition to a cloud-based AWS data lake, advocating modern architectures and capabilities Create and manage data infrastructure to enable data-driven decision making Design and implement automated pipelines for data and ML workflows with testing, versioning, and rollback Develop reusable processes/templates for packaging, testing, and deployment of internal Python libraries, containers, and ML workflows Stay current with industry trends and best practices in data and ML engineering and suggest improvements Apply Python and Sage Maker for analytics and ML initiatives Familiarity with AWS security and governance tools (IAM, KMS, Lake Formation, VPC) to ensure secure access Help shape Plymouth Rock’s data science/advanced analytics strategy and growth Key requirements 5-6 years of experience with Snowflake, DBT, Fivetran, AWS Glue, Python, Sage Maker, DB2, SQL Server, and Informatica in data engineering/analytics Bachelor in Computer Science, Information Systems or related field 3+ years in data platform or ML engineering focused on automated pipelines and workflows Experience building CI/CD pipelines with Jenkins, Git Hub Actions, Terraform, Cloud Formation or similar tools Experience automating workflows with AWS services (Lambdas, ECS, S3, Step Functions, Event Bridge)Experience with Docker and other containerization tools Expert in Python, proficient in SQL; knowledge of R and SAS a plus Data modeling and data warehousing experience Cloud data platforms experience (Snowflake preferred, AWS)Design thinking and test-driven development mindset Experience with BI tools (Tableau)Knowledge of Python packaging/distribution tools (pip, poetry, setuptools, twine)Knowledge of ETL/ELT, data lakes, data quality metrics Strong communication and cross-functional collaboration skills Curiosity and commitment to continuous learning in data engineering/toolscross-functional collaborationstrong communicationcuriosity/continuous learning SnowflakeDBTFivetran