Sr. Data Engineer
Listed on 2026-10-17
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IT/Tech
Data Engineering
life insurance, parental leave, paid time off, paid holidays, 401(k), stock options
Application close date:
Applications will be accepted on an ongoing basis until the requisition is closed.
At Blue Origin, we envision millions of people living and working in space for the benefit of Earth. We're working to develop reusable, safe, and low-cost space vehicles and systems within a culture of safety, collaboration, and inclusion. Join our team of problem solvers as we add new chapters to the history of spaceflight!
This role is part of Blue Origin Operations, which is comprised of Integrated Supply Chain, Test Operations, Safety, Quality, and Mission Assurance. This includes Manufacturing and Supply Chain support across all Blue Origin facilities.
As a senior technical contributor on the Supply Chain Business Intelligence & Data Science team, you will own the architecture and strategy for the highest-used supply chain data models at Blue Origin, and for the self-serve analytics platform built on top of them. Our team is a customer of Blue Origin's Enterprise Technology (ET) data platform - we consume their governed, foundational data - and we design, build, and own the business-facing data models in Databricks that sit on top of it, spanning procurement, inventory, logistics, and supplier performance.
These models are the backbone of self-serve analytics and AWS-hosted web applications used broadly across Supply Chain. This role blends deep data modeling expertise with architectural ownership of how the business self-serves insights, cross-team partnership with ET, and mentorship of a growing engineering team.
Key Responsibilities
Core Data Model Ownership: Architect and own the primary supply chain data models in Databricks - the most heavily used data assets across the organization - built on top of the data ET provides.
Self-Serve Analytics Strategy: Own the strategy and architecture for how supply chain stakeholders self-serve data - including semantic layers, curated data marts, access patterns, and documentation - ensuring business users across the org can reliably find and trust the metrics they need without engineering support.
ET Partnership & Data Strategy: Serve as the primary technical liaison with ET's data platform team, defining data contracts, advocating for supply chain data needs, and adapting the team's models as ET's underlying platform evolves.
Modeling Standards & Governance: Define and enforce data modeling standards, business logic ownership, and data quality frameworks that ensure trust and consistency across every downstream consumer of the team's supply chain models and self-serve tools.
Web Application & Delivery Strategy: Guide the architecture of AWS-hosted internal web applications and self-serve analytics tools that are built on top of the team's core data models, ensuring performance, scalability, and usability.
Technical Leadership: Set engineering standards for Git Lab CI/CD, VS Code-based development, testing, and deployment; mentor Data Engineer I-III team members on data modeling and self-serve enablement best practices.
AI-Enabled Engineering: Evaluate and integrate AI-assisted development, data modeling, and analytics tools (e.g., AI-powered query assistants, natural language interfaces) to further scale self-serve capabilities across the business.
Cross-Functional Partnership: Act as the primary technical point of contact between supply chain leadership and the engineering team, ensuring the team's data models and self-serve tools evolve to meet changing business needs.
End-to-End Coverage: Ensure comprehensive, reliable model and self-serve metric coverage across the entire supply chain function - procurement, inventory, logistics, supplier performance, and manufacturing supply - resolving gaps and inconsistencies as the business scales.
Minimum Qualifications
6+ years of experience building and operating production data pipelines and data models, including experience leading data modeling architecture decisions
Bachelor's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience
Expert-level proficiency in Python and SQL
Deep experience with Databricks (or comparable lakehouse platform) architecting large-scale, business-facing data models
Proven experience partnering with or consuming data from a centralized enterprise data platform team, including managing data contracts and adapting to upstream changes
Strong experience architecting solutions…
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