Engineering Data Architect

Haag, a Salas O'Brien Company•,

7d•$110,000 - $160,000•Remote

About The Position

The Engineering Data Architect establishes the data foundation for engineering technology, project delivery, analytics, automation, and AI. This hands-on role assesses the current environment, defines the target architecture and governance framework, and delivers practical data models, pipelines, integrations, analytics, and quality solutions.

Requirements

Bachelor’s degree in computer science, data engineering, information systems, engineering, or a related field, or equivalent experience.
Seven or more years of relevant experience across data architecture, data engineering, analytics, or data governance.
Experience delivering enterprise-scale data solutions and modern cloud analytical architectures.
Strong SQL, data-modeling, integration, and ETL/ELT skills, with hands-on pipeline and data-quality experience.
Knowledge of governance, metadata, lineage, stewardship, access, and lifecycle management.
Ability to convert architectural strategy into practical solutions and a realistic roadmap.
Strong analytical, facilitation, stakeholder-management, and communication skills.

Nice To Haves

Experience with Azure data services, Databricks, lakehouse architecture, Microsoft Fabric, Power BI, and Python.
AEC industry experience and familiarity with Revit, BIM, Autodesk Construction Cloud, Autodesk Forma, Autodesk Platform Services, or the AEC Data Model.
Experience integrating data with C#/.NET applications or collaborating with C#/.NET development teams.
Experience with metadata catalogs, observability, event-driven integrations, APIs, and data architecture for AI or machine learning.

Responsibilities

Assess engineering data sources, definitions, ownership, workflows, quality issues, dependencies, and integration gaps.
Define the target-state engineering data architecture, governance framework, and phased implementation roadmap.
Establish common models, taxonomies, metadata, lineage, stewardship, access, lifecycle, and quality standards.
Design and implement data models, ETL/ELT pipelines, integrations, semantic models, dashboards, and quality tools.
Deliver solutions using SQL, Python, Azure, Databricks, Power BI, and related technologies.
Integrate Revit, Autodesk Construction Cloud, Autodesk Forma, Autodesk Platform Services, and other engineering or business systems.
Partner across engineering and Digital Engineering to create reusable data assets for analytics, automation, AI, and reporting.
Coordinate data protection requirements, document decisions, evaluate platforms, and communicate priorities, risks, and outcomes.