Data Engineer

About the Role

We're looking for a Data Engineer to design and maintain the data architecture that powers our business systems and analytics. You'll possess a working knowledge of AI-powered applications as they become part of that landscape. You'll own how data is modeled, moved, and governed across the organization, ensuring a solid foundation for both traditional reporting and modern AI-driven tools. This role involves significant cross-functional collaboration with application teams, business stakeholders, and vendors to ensure data is structured consistently, documented clearly, and trustworthy.

Responsibilities

• Data architecture & schema design:
  • Design canonical, well-documented data models for core business entities (customers, projects, products, contracts, finance, etc.) as a single source of truth.
  • Evaluate and evolve schemas to balance new requirements with long-term maintainability.
  • Establish and enforce data modeling standards, naming conventions, and documentation practices.
• Pipelines & integration:
  • Build and maintain ETL/ELT pipelines to move data from source systems (ERP, CRM, operational databases, vendor feeds, files) into a cloud data warehouse.
  • Monitor data quality and pipeline health; resolve issues at the source.
  • Integrate new data sources, including third-party platforms and APIs, without duplicating effort or creating conflicting data versions.
• Data governance:
  • Define and maintain data classification standards for sensitive data (financial, contractual, customer) and ensure consistent application.
  • Support access control and audit requirements by ensuring traceable data lineage and usage.
  • Maintain a data dictionary and documentation for cross-team trust and usability.
• AI-aware data engineering:
  • Design data structures and access patterns suitable for AI/LLM-powered applications, focusing on well-scoped, accurate context.
  • Apply minimum-necessary-data principles for AI features in collaboration with application and security teams.
  • Stay current on retrieval-augmented generation and LLM context assembly to inform schema and access decisions.
• Collaboration:
  • Work with engineering, product, and business teams to translate needs into sound data models.
  • Act as a technical resource and point of contact for data-related questions across multiple projects.

Required Technical Skills

• 4+ years of experience in data engineering or data architecture, including hands-on schema/data model design in a production environment.
• Strong SQL skills and experience with cloud data warehouses (Snowflake and MSFT SQL Server preferred).
• Experience building and maintaining ETL/ELT pipelines.
• Solid understanding of data governance: classification, access control, and documentation.
• Working knowledge of how AI/LLM applications consume data (context windows, retrieval-augmented generation, data scoping).
• Strong communication skills; ability to serve as a data authority for cross-functional teams.

Preferred

• Experience in distribution, industrial supply, or ERP-adjacent environments.
• Familiarity with integrating third-party SaaS platforms via API.
• Experience with CI/CD tooling (e.g., GitHub Actions) and cloud environments (e.g., AWS).
• Experience consolidating multiple data models into a shared standard.

Nice to Have

• Exposure to BI/reporting tools (e.g., Power BI) and their data consumption patterns.
• Working knowledge of Python.

PSS Industrial provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristics protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.