Data Engineer II

Position Overview

Steer Bridge seeks a highly skilled and motivated Data Engineer II to join our team supporting the F-35 AI/ML Spares Project. As the most advanced fighter jet in the world, the F-35 strengthens national security, enhances global partnerships, and powers economic growth. Our F-35 AI/ML Spares Project applies advanced computational analytics to revolutionize supply chain management in the aerospace industry — harnessing the power of AI/ML to increase parts availability, reduce maintenance wait times, and maximize aircraft availability.

In collaboration with the National Center for Manufacturing Sciences (NCMS), we are on a mission to deliver exceptional solutions that will redefine operational readiness for the F-35 program and beyond.

In this mid-level role, you will build and maintain AWS-based ETL/ELT pipelines, curated analytical datasets, and reporting workflows that support operational decision‑making. You will design scalable data infrastructure that powers business intelligence, machine learning, and operational analytics — performing data engineering tasks on‑site within the existing systems of record with multiple databases. This position also involves mentoring and collaborating with Marines at the squadron level, requiring a deep understanding of squadron‑specific operations and a commitment to improving data entry and indexing practices.

You will be a crucial link between our existing systems and data development, collaborating closely with data scientists, analysts, and operational partners.

• Lead end‑to‑end data pipeline operations — design, develop, and maintain robust ETL/ELT pipelines on AWS (AWS Glue, Amazon Redshift, Amazon S3) using modern orchestration tools such as Apache Airflow, dbt, or Prefect.
• Use Azure Databricks (Spark) and Azure Data Factory to manage and schedule data pipelines and workflows.
• Build and optimize data models in cloud data warehouses (Snowflake, Big Query, or Redshift); maintain data in S3 buckets and Blob storage.
• Integrate data from diverse sources including REST/SOAP APIs, event streams (Kafka), relational and non‑relational databases, and SaaS platforms.
• Create, index, query, and update SQL tables/servers; run and update Python and/or JavaScript code to parse data.
• Monitor pipeline health, enforce data quality controls (schema validation, null checks, duplicate detection), troubleshoot data issues, and implement alerting and observability best practices.
• Develop and implement data acquisition, quality assurance, and management protocols; document all data collection, cleaning, and analyses for internal and external users.
• Use schedulers and APIs to obtain near real‑time data; automate workflows and processes using Python or other scripting languages.
• Partner with data scientists and analysts to deliver clean, well‑documented datasets and data products; collaborate with and support the data science team to produce deliverables.
• Contribute to data governance standards including lineage, cataloging, and access controls.
• Mentor and collaborate with Marines at the squadron level to improve data entry and indexing practices.
• Assist with maintenance and development of internal analytics data architecture.
• Design, write, and disseminate innovative and visually appealing reports for diverse audiences.
• Participate in code reviews, architecture discussions, and cross‑functional planning sessions.
• Evaluate and recommend new tools and technologies to improve the data platform.

• 3–5 years of professional experience in data engineering or a closely related role.
• Bachelor's Degree in Computer Science or related field; three (3) years of additional relevant experience may substitute for education (minimum six years total experience without degree).
• U.S. Citizenship required.
• Active security clearance or the ability to obtain one is required.

• Strong proficiency in Python (PySpark, pandas) and SQL for data processing and pipeline development; SQL fluency including CTEs, window functions, and complex joins.
• Hands‑on experience with at least one cloud data warehouse (Snowflake, Big Query,…