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
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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 alongside a Unified Namespace or MQTT-based data architecture; understands how industrial messaging infrastructure relates to the data layer
Familiarity with data Lakehouse platforms and open table formats (Delta Lake, Apache Iceberg, or similar)
Experience with ETL orchestration tooling (Airflow, Prefect, dbt, or similar)
Comfort with scripting and lightweight development (Python, SQL, or similar) for pipeline development and data quality tooling
Familiarity with cloud platforms (AWS, Azure, or GCP) and experience evaluating on-premises vs. cloud tradeoffs for data infrastructure
Experience working in a controlled information environment; familiarity with the handling requirements for Controlled Unclassified Information (CUI) or export-controlled technical data under ITAR or EAR
Experience in a manufacturing, industrial, or operations-heavy environment
About Vulcan Elements
Manufacturing
Vulcan Elements manufactures rare earth magnets for electric vehicles and defense applications, supporting domestic supply chain security and advanced manufacturing.
