DATA ENGINEER - DATA SCIENCE TEAM

Huntington Ingalls•Newport News, VA

2d•Hybrid

About The Position

We are looking for a highly skilled Data Engineer to join our Data Science team. In this role, you will collaborate closely with data scientists, analysts, and IT partners to build the data platforms, pipelines, and tooling needed to enable advanced analytics, machine learning, and enterprise-wide data-driven decision making. This is a senior-level engineering position responsible for designing, developing, and maintaining robust, scalable data solutions that power high‑impact models and analytical insights.

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.
Bachelor’s degree in Computer Science, Engineering, or related field.
5 years of experience in data engineering or software engineering with a focus on data-intensive applications. Master's degree and 3 years of relevant experience. PhD and 0 years of experience.
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.

Nice To Haves

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.

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.