Data Architect (Mid-Level)

Data Modeling & Architecture

Geospatial & Domain Data Design

Governance, Lineage & Data Quality

Implementation Partnership

Collaboration & Documentation

Requirements

• 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.

What We Offer

• Join a dynamic, growing company with a strong reputation in its industry.
• Competitive salary.
• Comprehensive health plan options, including medical, dental, and vision coverage.
• 401(k) plan with company match.
• Flexible PTO policy plus company-paid holidays.
• Additional benefits, including life insurance, pet insurance, and employee discounts and perks programs.
• Generous one-time work-from-home stipend to help you set up your home workspace.
• Company events and opportunities to connect, including monthly team lunches, volunteer outings, and quarterly gatherings.
• Hybrid employees have access to complimentary snacks, beverages, and coffee at our Columbus, Ohio office.

Preferred Qualifications

• Experience with at least one major cloud platform; Azure experience preferred.
• 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.
• 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
• 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.

Responsibilities

• Design the schema and unified data model for the platform, spanning asset, inspection, incident, geospatial, and consequence data across multiple products.
• Apply medallion architecture patterns (Bronze, Silver, Gold) and define the semantic layer consumed by downstream models, reporting, and applications.
• Model the data required by probabilistic risk models, ensuring every required input attribute has a defined source, transformation path, and data-quality expectation.
• Design cross-product entity resolution so records from separate products reconcile to shared assets and entities.
• Define ingestion patterns for batch, streaming, and change-data-capture (CDC) sources, including schema evolution and slowly changing dimensions.
• Produce architecture documentation, diagrams, data dictionaries, and decision records that engineers can implement without ambiguity.
• Design the shared GIS layer as a first-class component of the data model, including pipeline centerlines, consequence-area polygons, right-of-way corridors, and parcel data.
• Model linear referencing and dynamic segmentation so risk results align with centerline geometry and can be aggregated across differing segmentations.
• Define data structures for inspection data, repair history, and external enrichment sources such as weather history, soil characteristics, and satellite-derived data.
• Define cataloging, classification, and metadata standards in Unity Catalog and establish expectations for lineage coverage across the data estate.
• Establish data-quality rules, validation gates, and profiling standards, including processes for quarantining and remediating failures.
• Define access-control patterns, including role-based and attribute-based access, tenant isolation, and appropriate handling of sensitive data.
• Design for end-to-end traceability so that any risk output can be traced through every transformation back to its source record.
• Work directly with data engineers to translate architectural designs into production pipelines and review implementations for conformance.
• Contribute hands-on to schema deployment, transformation logic, and optimization when it accelerates delivery.
• Partner with application engineers and data scientists to determine how the data model is consumed through serving layers, feature stores, and APIs.
• Support performance and cost optimization, including partitioning, clustering, file layout, and compute sizing.
• Participate in design reviews and architecture alignment sessions and defend design decisions using data and evidence.
• Work with subject-matter experts to translate domain and regulatory requirements into concrete data structures and standards.
• Maintain architecture documentation as the model evolves, treating stale or inaccurate documentation as a defect.