Applied Machine Learning Engineer

Vulcan Elements seeks an Applied Machine Learning Engineer in Durham, NC (onsite) to lead data infrastructure and pipeline work that supports operational, analytics, and AI/ML workloads.

Responsibilities

• Design and own 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, balancing scalability, compliance requirements, operational burden, and cost
• Review, refine, and implement data architecture design documents with CUI and ITAR data handling requirements in mind
• Make and document platform and design decisions so future team members can understand the rationale and extend the work
• Ensure the architecture scales from pilot plant to full-scale facility without fundamental redesign
• Apply engineering standards across the stack: version control, testing, observability, and documentation, and maintain those expectations as the data team grows
• Design and build ETL/ELT pipelines that move operational data into the data Lakehouse with contextual enrichment for analytics and AI/ML use
• Create reliable ingest paths for structured data, time-series data, files, images, and other outputs from manufacturing and lab systems
• Collaborate with engineering, operations, and IT to understand data flows, dependencies, and integration needs, then translate them into pipeline and architecture decisions
• Identify and eliminate manual data workflows by replacing them with monitored, dependable pipelines
• Diagnose and resolve data quality issues across the stack and build monitoring into pipelines so issues surface early
• Define data models for operational queries, analytical workloads, and future AI and ML applications
• Own data contextualization standards so each data point includes the metadata required to make it meaningful
• Contribute to schema design and payload definitions for operational data stores to support consistency and legibility
• Support development of reporting and visibility tools for operational and leadership insight into process and quality data
• Write clear technical documentation covering architecture decisions, data models, pipeline designs, and operational runbooks

Requirements

• 8+ years of experience in data engineering, data infrastructure, or a closely related role with a record of owning and delivering production systems
• Experience designing and building data lakes, Lakehouses, or analytical data stores, including tradeoffs and the ability to defend platform selection
• Strong ETL/ELT pipeline experience for enriching and contextualizing data
• Deep fluency in data modeling for both operational and analytical workloads
• Experience with relational databases such as PostgreSQL or SQL Server; confident SQL development and debugging
• Comfort working in a fast-moving, small-team environment; able to make decisions with incomplete information and document them clearly
• Strong communication skills across technical and non-technical stakeholders, translating operational requirements into architecture decisions
• Must be a U.S. Person to access required U.S. export-controlled information or facilities

Technologies

• PostgreSQL
• SQL Server
• InfluxDB
• TimescaleDB
• Delta Lake
• Apache Iceberg
• Airflow
• Prefect
• dbt
• Python
• SQL
• AWS
• Azure
• GCP
• MQTT
• CUI
• ITAR
• EAR

Desired Skills

• Experience with time-series databases such as InfluxDB, TimescaleDB, or similar
• Familiarity with industrial data concepts (historian data, process tags, OT/IT integration) and manufacturing data challenges
• Experience with a Unified Namespace or MQTT-based data architecture
• Familiarity with 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 AWS, Azure, or GCP, including evaluating on-premises versus cloud tradeoffs
• Experience in controlled information environments, including CUI and export-controlled technical data under ITAR or EAR
• Experience in manufacturing, industrial, or operations-heavy environments

Role Location

• Onsite in Durham, NC at start
• Expected to move to Benson, NC upon completion of a new facility