Data Engineer

Responsibilities

• Evaluate and select platforms for the data Lakehouse, ETL tooling, and operational databases, weighing scalability, compliance requirements, operational burden, and cost.
• Review, refine, and implement data architecture design documents, ensuring designs are technically sound and account for CUI and ITAR data handling requirements.
• Make and document key platform and design decisions with enough clarity that future team members can understand the reasoning and build on it.
• Ensure the architecture scales from pilot plant to full-scale facility without fundamental redesign.
• Apply sound engineering practices to everything you build: version control, testing, observability, and documentation, and hold those standards as the data team grows.
• Design and build ETL pipelines that move data from operational data stores into the data Lakehouse with full contextual enrichment, making it ready for analytics and AI workloads.
• Collaborate across engineering, operations, and IT to understand data flows, dependencies, and integration requirements, and translate them into pipeline and architecture decisions.
• Identify and eliminate manual data workflows, replacing them with monitored, reliable pipelines.
• Diagnose and resolve data quality issues across the stack, and build monitoring into pipelines so problems surface early.
• Define data models that support operational queries, analytical workloads, and future AI and ML applications.
• Own data contextualization standards ensuring every data point carries the metadata needed to make it meaningful.
• Contribute to schema design and payload definitions for operational data stores, working toward consistency and legibility across the organization.
• Support the development of reporting and visibility tools that give operations and leadership clear insight into process and quality data.
• Write clear technical documentation for architecture decisions, data models, pipeline designs, and operational runbooks.

Qualifications

• 8+ years of experience in data engineering, data infrastructure, or a closely related technical role with a track record of owning and delivering production systems.
• Demonstrated experience designing and building data lakes, Lakehouses, or analytical data stores; understands the tradeoffs between platforms and can make and defend platform selection decisions.
• Strong experience designing and building ETL/ELT pipelines that enrich and contextualize data.
• Deep fluency with data modeling for both operational and analytical workloads; can design schemas that serve present needs without foreclosing future ones.
• Experience with relational databases (PostgreSQL, SQL Server, or similar); writes and debugs SQL confidently.
• Comfortable working in a fast-moving environment with a small team, making decisions with incomplete information and documenting them clearly for future colleagues.
• Strong communicator who can work across technical and non-technical stakeholders and translate between operational requirements and data architecture decisions.
• Must be a U.S. Person due to required access to U.S. export-controlled information or facilities.