DATA ENGINEER - DATA SCIENCE TEAM

About the role

The anticipated salary range represents the estimated base pay for this role. Final compensation will be determined based on experience, qualifications, and other job-related factors, and may vary from the posted range.

Responsibilities

• Architect, build, and optimize data pipelines and workflows that support machine learning, statistical modeling, and analytics use cases.
• Partner with data scientists to operationalize models, including feature engineering pipelines, model ingestion, and production deployment patterns.
• Design and maintain scalable data environments (data lakes, warehouses, streaming platforms) with a focus on performance, security, and data quality.
• Develop and enforce best practices for data governance, workflow orchestration, documentation, and code quality.
• Conduct root-cause analysis on data issues and improve reliability, observability, and monitoring of data systems.
• Mentor junior engineers and contribute to team-wide technical standards, patterns, and reusable components.
• Collaborate with cross-functional teams to understand data requirements and translate them into robust engineering solutions.

Requirements

• Bachelor's Degree and 5 years of relevant exempt experience; Master's Degree and 3 years of relevant professional experience; Ph.D. and 0 years of experience.
• One of the following may be used as an equivalent to Bachelor's Degree for Information Technology Related Positions Only:
• NNS Apprentice School graduate
• Navy Nuclear Power School (NNPS) graduate
• Associate's Degree or other formal 2 year program and 2 years of relevant exempt experience or 4 years of relevant non-exempt experience
• Military Paygrade E-5 or above military experience
• High School/GED and 4 years combined of Manufacturing, Shipbuilding, Trades, Military experience or other relevant exempt experience
• High School/GED and 8 years combined of Manufacturing, Shipbuilding, Trades, Military experience or other relevant non-exempt experience
• A relevant professional certification can be substituted for a Bachelor's Degree.
• Strong proficiency in Python, SQL, and modern data engineering frameworks.
• Hands-on experience with cloud data platforms (Azure, AWS, GCP).
• Expertise with distributed data technologies (Spark, Databricks, Hadoop, Kafka, etc.).
• Experience developing production-grade ETL/ELT pipelines and orchestration tools (Airflow, Azure Data Factory, Dagster).
• Familiarity with machine learning workflows and supporting data science teams.

Qualifications

• Experience deploying and monitoring machine learning models in production.
• Knowledge of MLOps frameworks (MLflow, Kubeflow, Vertex AI, Azure ML).
• Exposure to CI/CD, containerization (Docker), and infrastructure-as-code (Terraform).
• Strong communication skills and the ability to work effectively in technical and business-facing discussions.