Data Engineer

PSS Industrial Group•Houston, TX

8d•Onsite

About The Position

We're looking for a Data Engineer to design and maintain the data architecture that powers our business systems and analytics — and 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, and make sure that foundation is solid enough for both traditional reporting and modern AI-driven tools to build on. This role sits at the center of a lot of cross-functional work: you'll partner with application teams, business stakeholders, and vendors to make sure data is structured consistently, documented clearly, and trustworthy wherever it's used.

Requirements

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 a cloud data warehouse (Snowflake and MSFT SQL Server preferred)
Experience SQL query language
Experience building and maintaining ETL/ELT pipelines.
Solid understanding of data governance fundamentals: classification, access control, and documentation practices.
Working knowledge of how modern AI/LLM applications consume data — context windows, retrieval-augmented generation, and the basics of why minimizing and scoping data matters for these systems.
Strong communication skills; comfortable being the person other teams come to for data questions.

Nice To Haves

Exposure to BI/reporting tools (e.g., Power BI) and how they consume the underlying data model.
Working knowledge of Python

Responsibilities

Design canonical, well-documented data models for core business entities (customers, projects, products, contracts, finance, etc.) that multiple systems and teams can rely on as a single source of truth.
Evaluate and evolve schemas as the business and its systems grow — balancing new requirements against long-term maintainability.
Establish and enforce data modeling standards, naming conventions, and documentation practices.
Build and maintain ETL/ELT pipelines that move data reliably from source systems (ERP, CRM, operational databases, vendor feeds, files) into a cloud data warehouse.
Monitor data quality and pipeline health; troubleshoot and resolve data issues at the source rather than downstream.
Integrate new data sources — including third-party platforms and vendor APIs — into the existing architecture without duplicating effort or creating conflicting versions of the same data.
Define and maintain data classification standards (e.g., what counts as sensitive financial, contractual, or customer data) and ensure those standards are applied consistently across systems.
Support access control and audit requirements by ensuring data lineage and usage are traceable.
Maintain a data dictionary and related documentation so other teams can find and trust the data they need.
Design data structures and access patterns with an understanding of how they'll be consumed — including by AI/LLM-powered applications that rely on well-scoped, accurate context rather than raw database access.
Apply minimum-necessary-data principles when structuring data that will be surfaced through AI features, in partnership with application and security teams.
Stay current enough on how retrieval-augmented generation and LLM context assembly work to make informed schema and access decisions — this is a working-knowledge requirement, not a machine learning role.
Work with engineering, product, and business teams to translate reporting and application needs into sound data models.
Act as a technical resource and point of contact for data-related questions across multiple concurrent projects.