Lead Data Engineer – Enterprise Analytics & Cloud Data Lake

plymouthrock; Boston (MA); On-site; USD 134,000 - 187,000; Full time

Benefits offered by this job

Free onsite gym in Boston

Tuition Reimbursement

401(k) employer contribution

Health insurance starting Day 1

Paid time off and holidays

Parental leave

Commuter benefits

Job summary

Plymouth Rock is seeking a Senior Data Engineer to lead the Enterprise Advanced Analytics data pipelines from Boston. You will architect and optimize pipelines, enabling data scientists to derive actionable insights that drive growth and efficiency.

Join a high-visibility team focused on cloud-native architectures, data lake implementations, and scalable ML infrastructure. Collaborate across IT, Data Science, and business units to advance enterprise analytics and innovation.

Qualifications

• Master's degree in CS, IS or related field.
• 5+ years in data/data platform or ML engineering with automated pipelines.
• Experience with CI/CD pipelines (Jenkins, GitHub Actions, Terraform, CloudFormation).
• Experience with AWS services (Lambdas, ECS, S3, Step Functions, EventBridge).
• Experience with Snowflake and cloud data platforms.

Responsibilities

• Lead data engineering for Enterprise Advanced Analytics; design, build, and optimize data pipelines.
• Collaborate with Data Science, IT, and Business to automate ML workflows across units.
• Partner with IT on transitioning to an AWS data lake with modern architectures.
• Develop reusable data infra and packaging/testing for pipelines and ML workflows.
• Stay current with industry trends and guide Plymouth Rock's analytics strategy.

Skills

Python

SQL

CI/CD

AWS

Snowflake

Education

Master's degree in CS/IS

Tools

Jenkins

GitHub Actions

Terraform

CloudFormation

Docker

Job description

Plymouth Rock is seeking a Senior Data Engineer to lead the Enterprise Advanced Analytics data pipelines from Boston. You will architect and optimize pipelines, enabling data scientists to derive actionable insights that drive growth and efficiency.

Join a high-visibility team focused on cloud-native architectures, data lake implementations, and scalable ML infrastructure. Collaborate across IT, Data Science, and business units to advance enterprise analytics and innovation.