Data Architect (Mid-Level)

Data Architect – Insights (AI/ML)

Required Qualifications

• 5–8 years of experience in data architecture, data modeling, or senior data engineering, including end-to-end ownership of a non-trivial data model.
• Hands-on experience with Databricks, including Delta Lake, Unity Catalog, SQL Warehouses, and pipeline orchestration.
• Strong command of medallion and lakehouse architecture patterns, dimensional modeling, and semantic layer design.
• Advanced SQL skills and proficiency in Python or PySpark.
• Experience with at least one major cloud platform; Azure experience preferred.
• Practical experience with change data capture (CDC), slowly changing dimensions (SCD), schema evolution, and data-quality frameworks.
• Working knowledge of data governance, including cataloging, lineage, classification, role-based access control (RBAC), and encryption.
• Strong documentation and diagramming skills, with the ability to explain data models clearly to both technical and non-technical stakeholders.

Preferred Qualifications

• Experience designing geospatial data models and working with GIS tooling, spatial joins, and linear referencing.
• Experience with multi-tenant platforms, data residency requirements, or regulated environments.
• Familiarity with metadata and lineage tools such as Unity Catalog, Microsoft Purview, or equivalent platforms.
• Experience modeling data specifically for machine learning or probabilistic risk models.
• Experience designing semantic layers for business intelligence (BI) consumption.
• Databricks or cloud data certifications.
• Experience using AI-assisted coding tools such as Cursor or GitHub Copilot and/or agentic coding tools such as Claude Code as part of a professional development workflow.

Nice to Have

• Familiarity with pipeline or utility asset data, including inline inspection results, alignment sheets, facilities, and centerline geometry.
• Understanding of asset integrity concepts such as corrosion growth, defect tracking, and consequence-of-failure modeling.
• Awareness of regulatory reporting requirements applicable to pipeline integrity data.
• Experience migrating data from legacy desktop or spreadsheet-based systems.

Success Metrics

Success in this role will be measured by:

• A documented and implemented data model that supports every required risk-model input attribute with a clearly defined source and transformation path.
• High lineage coverage and demonstrable traceability from risk outputs back to the originating source records.
• Data engineers consistently able to build from the architecture and documentation without repeated clarification.
• Reliable cross-product entity resolution across shared assets and entities.