Applied Machine Learning Engineer

Vulcan Elements; North Carolina; On-site; USD 120,000 - 180,000; Full time

Job summary

Vulcan Elements is building a team of ambitious professionals in Durham, NC, with plans to move to Benson, NC for a new facility. The Data Engineer will design and implement data infrastructure, from operational stores through ETL pipelines to analytics and AI layers, ensuring scalable, compliant data flows across manufacturing and lab systems.

Responsibilities include platform evaluation, data modeling, and building reliable ingestion paths.

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 platform tradeoffs and can defend platform choices.
• 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 for present and future needs.
• Experience with relational databases (PostgreSQL, SQL Server, or similar); writes and debugs SQL confidently.
• Able to work in a fast-moving environment with a small team and document decisions clearly for future colleagues.
• Must be a U.S. Person due to access to U.S. export-controlled information or facilities.

Responsibilities

• Design and own Vulcan’s data architecture from operational data stores through ETL pipelines to analytics and AI layers.
• Evaluate and select platforms for a data lakehouse, ETL tooling, and operational databases, balancing scalability and cost.
• Review, refine, and implement data architecture documents with clear reasoning; ensure compliance with CUI and ITAR data handling.
• Document key platform decisions so future teammates can build on them.
• Scale architecture from pilot plant to full-scale facility without major redesigns.
• Apply engineering practices: version control, testing, observability, and documentation as the team grows.
• Design and build ETL pipelines that move data into the data lakehouse with full contextual enrichment for analytics/AI.
• Build reliable ingest paths for structured, time-series data, files, images, and lab/manufacturing outputs.
• Collaborate with engineering, operations, and IT to translate data flows into pipelines and architecture.
• Identify and eliminate manual workflows; monitor pipelines for early issue surface.
• Diagnose data quality issues and build monitoring into pipelines.

Skills

Data engineering

ETL/ELT pipelines

Data lakes/Lakehouse

SQL databases

Data modeling

Stakeholder comms

US Person

Python

Tools

Airflow

dbt

SQL

Delta Lake

Apache Iceberg

InfluxDB

TimescaleDB

Job description

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 theData 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 durable, scalable data architecture. As Vulcan grows, this role has the opportunity to expand into a team leadership position.

This role begins in Durham, NC and is expected to move to Benson, NC upon completion of new facility.

Responsibilities

• 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
• 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 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

As set forth in Vulcan Elements’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

This position requires access to information and technology that is subject to US Export Control. Vulcan Elements will use this information to determine if you are eligible to receive this information under US Law.