Data Engineer

Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, we serve critical industries such as defense, aerospace, and automotive, powering a high-technology future. Vulcan Elements is building a team of ambitious professionals committed to Mission Focus, Technical Excellence, and Transparency.

As the Data Engineer, you will design and build the data infrastructure that makes Vulcan’s operational and business data useful — first at pilot scale, and then as the foundation for a 10,000 ton/year facility. You will work from architecture to implementation: evaluating and selecting platforms, designing data models and pipelines, and building the systems that collect, contextualize, and deliver data to the teams and tools that depend on it. You will collaborate closely with cross-functional stakeholders to translate operational requirements into a durable, scalable data architecture. As Vulcan grows, this role has the opportunity to expand into a team leadership position.

Responsibilities

Architecture Platform Design

Design and own Vulcan’s data architecture from operational data stores through ETL pipelines to the analytics and AI layer

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

Data Pipeline Integration

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

Build reliable ingest paths for structured data, time-series data, files, images, and other outputs from manufacturing and lab systems

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

Data Modeling Quality

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

Responsibilities and tasks outlined are not exhaustive and may change as determined by the needs of the business.

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

Desired Skills

Experience with time-series databases (InfluxDB, TimescaleDB, or similar) common in industrial and IoT environments

Familiarity with industrial data concepts — historian data, process tags, OT/IT integration — and the data challenges specific to manufacturing environments

Experience working on or a…