{"id":577097,"url":"https://alion.io/job/irth-solutions-data-architect-mid-level","title":"Data Architect (Mid-Level)","company":{"id":7550,"name":"Irth Solutions","domain":"irthsolutions.com","url":"https://alion.io/company/irth-solutions","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":60,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":117000,"max_usd":231000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":465},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"Feature Store","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"AI Agents","optional":true},{"name":"Claude Code","optional":true},{"name":"Copilot","optional":true},{"name":"Cursor","optional":true},{"name":"Machine Learning","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-09T00:00:00Z","employer_posted_date":"2026-09-09","last_verified_at":"2026-10-28T00:48:03Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":49},"description":"Data Architect - Insights (AI/ML)\nLocation: Remote (US)\nDepartment: Insights (AI/ML)\nReports to: Engineering Manager\nAbout the Role\nIrth 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:\nA governed, cross-product data platform built on Databricks and Azure\nAn AI-powered ingestion layer that normalizes, repairs, and enriches customer data without services-heavy onboarding\nA reusable analytical layer that runs industry-standard, Irth-developed, and customer-built risk models against the data\nWe are seeking a mid-level Data Architect to design the schema and data model that powers this platform. You will create a unified structure spanning asset integrity, damage prevention, and land and stakeholder management, capable of supporting probabilistic risk models that require dozens of precisely defined input attributes per pipeline segment.\nThis is a hands-on design role. You will establish standards for data lineage, governance, and quality while working closely with the data engineering team to implement them. This role is ideal for someone with substantial experience in lakehouse and medallion architecture who is ready to own the data model for a platform rather than a single project.\nKey Responsibilities\n1. Data Modeling & Architecture\nDesign the schema and unified data model for the platform, spanning asset, inspection, incident, geospatial, and consequence data across multiple products.\nApply medallion architecture patterns (Bronze, Silver, Gold) and define the semantic layer consumed by downstream models, reporting, and applications.\nModel the data required by probabilistic risk models, ensuring every required input attribute has a defined source, transformation path, and data-quality expectation.\nDesign cross-product entity resolution so records from separate products reconcile to shared assets and entities.\nDefine ingestion patterns for batch, streaming, and change-data-capture (CDC) sources, including schema evolution and slowly changing dimensions.\nProduce architecture documentation, diagrams, data dictionaries, and decision records that engineers can implement without ambiguity.\n2. Geospatial & Domain Data Design\nDesign 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.\nModel linear referencing and dynamic segmentation so risk results align with centerline geometry and can be aggregated across differing segmentations.\nDefine data structures for inspection data, repair history, and external enrichment sources such as weather history, soil characteristics, and satellite-derived data.\n3. Governance, Lineage & Data Quality\nDefine cataloging, classification, and metadata standards in Unity Catalog and establish expectations for lineage coverage across the data estate.\nEstablish data-quality rules, validation gates, and profiling standards, including processes for quarantining and remediating failures.\nDefine access-control patterns, including role-based and attribute-based access, tenant isolation, and appropriate handling of sensitive data.\nDesign for end-to-end traceability so that any risk output can be traced through every transformation back to its source record.\n4. Implementation Partnership\nWork directly with data engineers to translate architectural designs into production pipelines and review implementations for conformance.\nContribute hands-on to schema deployment, transformation logic, and optimization when it accelerates delivery.\nPartner with application engineers and data scientists to determine how the data model is consumed through serving layers, feature stores, and APIs.\nSupport performance and cost optimization, including partitioning, clustering, file layout, and compute sizing.\n5. Collaboration & Documentation\nParticipate in design reviews and architecture alignment sessions and defend design decisions using data and evidence.\nWork with subject-matter experts to translate domain and regulatory requirements into concrete data structures and standards.\nMaintain architecture documentation as the model evolves, treating stale or inaccurate documentation as a defect.\nRequirements\nRequired Qualifications\n5-8 years of experience in data architecture, data modeling, or senior data engineering, including end-to-end ownership of a non-trivial data model.\nHands-on experience with Databricks, including Delta Lake, Unity Catalog, SQL Warehouses, and pipeline orchestration.\nStrong command of medallion and lakehouse architecture patterns, dimensional modeling, and semantic layer design.\nAdvanced SQL skills and proficiency in Python or PySpark.\nExperience with at least one major cloud platform; Azure experience preferred.\nPractical experience with change data capture (CDC), slowly changing dimensions (SCD), schema evolution, and data-quality frameworks.\nWorking knowledge of data governance, including cataloging, lineage, classification, role-based access control (RBAC), and encryption.\nStrong documentation and diagramming skills, with the ability to explain data models clearly to both technical and non-technical stakeholders.\nPreferred Qualifications\nExperience designing geospatial data models and working with GIS tooling, spatial joins, and linear referencing.\nExperience with multi-tenant platforms, data residency requirements, or regulated environments.\nFamiliarity with metadata and lineage tools such as Unity Catalog, Microsoft Purview, or equivalent platforms.\nExperience modeling data specifically for machine learning or probabilistic risk models.\nExperience designing semantic layers for business intelligence (BI) consumption.\nDatabricks or cloud data certifications.\nExperience 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.\nNice to Have\nFamiliarity with pipeline or utility asset data, including inline inspection results, alignment sheets, facilities, and centerline geometry.\nUnderstanding of asset integrity concepts such as corrosion growth, defect tracking, and consequence-of-failure modeling.\nAwareness of regulatory reporting requirements applicable to pipeline integrity data.\nExperience migrating data from legacy desktop or spreadsheet-based systems.\nSuccess Metrics\nSuccess in this role will be measured by:\nA documented and implemented data model that supports every required risk-model input attribute with a clearly defined source and transformation path.\nHigh lineage coverage and demonstrable traceability from risk outputs back to the originating source records.\nData engineers consistently able to build from the architecture and documentation without repeated clarification.\nReliable cross-product entity resolution across shared assets and entities.","description_format":"text","description_chars":6952,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Professional development"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Low-Code & No-Code Platforms","Field Service Management Software"],"lifecycle":[{"event":"open","at":"2026-09-09T18:30:09Z"}],"visa":[],"liveness":{"score":65,"band":"ok","label":"Likely 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