Data Architect (Mid-Level)

Irth; United States; À distance; USD 140 000 - 190 000; Plein temps

Avantages offerts par ce poste

Health plan

401(k) match

Flexible PTO

Work-from-home stipend

Team events

Résumé du poste

Irth is hiring a Data Architect - Insights (AI/ML) to design the platform's unified data model spanning assets, inspections, incidents, geospatial data and more. The role focuses on medallion architecture, data governance, and scalable ingestion patterns to support probabilistic risk models and downstream analytics.

You will collaborate with data engineers and data scientists to ensure traceability, performance, and cost efficiency across end-to-end pipelines, serving layers, and BI-ready

Qualifications

• 5–8 years of experience in data architecture or senior data engineering with end-to-end ownership of a non-trivial data model
• Hands-on Databricks experience including Delta Lake, Unity Catalog, SQL Warehouses, and pipeline orchestration
• Strong command of medallion and lakehouse 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 CDC, slowly changing dimensions, schema evolution, and data-quality frameworks
• Working knowledge of data governance, including cataloging, lineage, RBAC, and encryption
• Strong documentation and diagramming skills with clear explanations for stakeholders

Responsabilités

• Data Modeling & Architecture across asset, inspection, incident, geospatial, and consequence data
• Apply medallion architecture and define semantic layer for downstream models and reporting
• Model data for probabilistic risk models with defined sources and transformations
• Design cross-product entity resolution for shared assets and entities
• Define ingestion patterns for batch, streaming, and CDC sources with schema evolution
• Produce architecture docs, diagrams, data dictionaries, and decision records
• Design shared GIS layer and linear referencing for risk alignment with centerlines
• Work with data engineers to translate designs into production pipelines and optimize as needed
• Partner with application data scientists to align serving layers, feature stores, and APIs
• Support performance and cost optimization and participate in architecture reviews
• Maintain architecture docs and domain requirements with subject-matter experts

Connaissances

Databricks

Delta Lake

Unity Catalog

SQL

Python

PySpark

Azure

CDC & SCD

Outils

Databricks

Delta Lake

Unity Catalog

SQL

Python

PySpark

Azure

Description du poste

About The Role

Irth is building a new AI-driven threat and risk management platform for pipeline asset integrity. The platform brings together three capabilities that have historically been separate at Irth:

• A governed, cross-product data platform built on Databricks and Azure
• An AI-powered ingestion layer that normalizes, repairs, and enriches customer data without services-heavy onboarding
• A reusable analytical layer that runs industry-standard, Irth-developed, and customer-built risk models against the data

Data Architect - Insights (AI/ML)

Location: Remote (US)

Department: Insights (AI/ML)

Reports to: Engineering Manager

Key Responsibilities

• Data Modeling & Architecture
• 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

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

Benefits

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