Applied Machine Learning Engineer

Vulcan Elements is hiring an Applied Machine Learning Engineer (onsite) to design and deliver the data infrastructure and pipelines that support operational analytics and AI/ML workloads. The role spans platform selection, data architecture, ETL/ELT development, and data quality practices with required handling for CUI and ITAR data.

Key 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, considering scalability, compliance needs, operational burden, and cost
• Review, refine, and implement data architecture design documents, ensuring technical soundness and CUI and ITAR data handling requirements are addressed
• Make and document key platform and design decisions with sufficient clarity to support future team members
• Ensure the architecture can scale from pilot plant to full-scale facility without requiring fundamental redesign
• Apply engineering practices across version control, testing, observability, and documentation, and maintain these standards as the data team grows
• Design and build ETL pipelines moving data from operational data stores into the data Lakehouse with contextual enrichment for analytics and AI workloads
• Build dependable 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 remove manual data workflows, replacing them with monitored and reliable pipelines
• Diagnose and resolve data quality issues across the stack, with monitoring designed into pipelines to surface problems 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 improve consistency and legibility
• Support reporting and visibility tools that provide operations and leadership with clear insight into process and quality data
• Write technical documentation covering architecture decisions, data models, pipeline designs, and operational runbooks

Required Qualifications

• 8+ years of experience in data engineering, data infrastructure, or a closely related technical role, with a proven track record of owning and delivering production systems
• Experience designing and building data lakes, Lakehouses, or analytical data stores; ability to weigh platform tradeoffs and defend selection decisions
• Strong experience designing and building ETL/ELT pipelines that enrich and contextualize data
• Deep fluency in data modeling for both operational and analytical workloads, able to design schemas that meet present needs while preserving future options
• Experience with relational databases (PostgreSQL, SQL Server, or similar), with confidence writing and debugging SQL
• Comfort operating in a fast-moving environment with a small team, making decisions with incomplete information and documenting them for future colleagues
• Strong communication skills across technical and non-technical stakeholders, translating operational needs into data architecture decisions
• Must be a U.S. Person due to required access to U.S. export-controlled information or facilities

Technology Stack

• 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 (InfluxDB, TimescaleDB, or similar) used in industrial and IoT contexts
• Familiarity with industrial data concepts such as historian data, process tags, and OT/IT integration, including manufacturing-focused data challenges
• Experience working with or alongside a Unified Namespace or MQTT-based data architecture, and understanding how industrial messaging infrastructure maps to the data layer
• Familiarity with data Lakehouse platforms and open table formats (Delta Lake, Apache Iceberg, or similar)
• Experience with ETL orchestration tools (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, GCP) and experience evaluating on-premises versus cloud tradeoffs for data infrastructure
• Experience working in a controlled information environment, including familiarity with handling requirements for CUI or export-controlled technical data under ITAR or EAR
• Experience in manufacturing, industrial, or operations-heavy environments

Location

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