Job Description

Skills / Experience

• 8+ years in enterprise data modeling with 3+ years leading modeling on complex, multi-source data platform programs; Deep expertise in logical and physical data modeling - dimensional modeling (star/snowflake schemas), normalized models, and Snowflake-native wide-table patterns
• Hands-on Snowflake experience - schema design, clustering strategies, materialization types, and query performance fundamentals; Strong command of dbt - translating logical model designs into modular dbt model structures, YAML schema definitions, and test coverage
• Experience integrating data models with a metadata governance platform (Collibra preferred) - business glossary, attribute-level lineage, and data contract definition; Demonstrated ability to produce and maintain data dictionaries, ERDs, and source-to-target mapping documents across multi-domain enterprise programs
• Experience in a regulated industry (insurance, healthcare, or financial services) with awareness of PII/PHI data handling and compliance requirements
• Tech Stack Snapshot Snowflake, dbt (data build tool), AWS S3, Matillion, Collibra, Profisee MDM, Python, SQL, Snowflake Data Metric Functions (DMFs), SnowConvert AI, ACORD Data Model, CI/CD (Azure DevOps), Git

Good to Have

• Familiarity with ACORD Life & Annuity data standards - Policy, Claims, Party, Product, Actuarial domain entities
• Insurance domain knowledge across life insurance, annuities, reinsurance, or employee benefits product lines
• SnowPro Core or SnowPro Advanced: Data Engineer certification
• Exposure to Profisee MDM or equivalent enterprise MDM platforms and how golden record schemas integrate into Lakehouse layers

Data Model Design & Governance Design and own target-state logical and physical data models for all 9 Wave 1 certified data products: Policy & Contract Management, Premium & Billing, Claims & Benefits, Actuarial & Reserves, Financial Accounting, Agent & Distribution, Customer & Party (MDM), Product Master (RDM), and Compliance & Regulatory; Establish enterprise modeling standards - naming conventions, schema versioning, referential integrity patterns, and data type governance - across the Snowflake Silver and Gold layers; Own ACORD Life & Annuity data model customization for the customer's specific domain structure, translating insurance industry standards into implementable Snowflake schemas; Review and approve all dbt model definitions, ensuring alignment with the approved logical model and Collibra-registered metadata contracts

Snowflake Schema & Medallion Architecture Design Snowflake-native dimensional, normalized, and wide-table schemas optimized for query performance, downstream BI consumption (Tableau, Power BI), and Snowflake Cortex AI workloads; Collaborate with Senior Data Engineers to align physical schema design with Matillion ingestion patterns, dbt transformation layers, and Snowflake Data Metric Function (DMF) quality coverage; Define clustering keys, materialization strategies (tables, views, dynamic tables), and schema partitioning patterns per domain to support the program's 10 50x query performance improvement targets; Drive source-to-target mapping completeness across 347 SQL Server and 25 Oracle legacy systems, supporting the 7-year historical data migration

Collibra Integration & Data Lineage Define business glossary entities and attribute-level metadata in Collibra corresponding to each certified data product's physical model; Govern end-to-end lineage registration - source Matillion ingestion dbt transformation Snowflake Gold Tableau/Power BI - for all modeled entities; Define data contracts (agreed schema, SLOs, quality rules) for each certified data product published to the Gold layer; Collaborate with the MDM/Governance Specialist and Technical Architect to ensure Profisee golden record schemas integrate cleanly into the Silver layer dimensional model

Technical Leadership & Standards Produce and maintain data dictionaries, ERDs, schema change management procedures, and model versioning documentation as living, version-controlled artifacts; Conduct model design reviews with data engineers and technical leads before sprint delivery - identifying schema drift before it becomes rework; Partner with the Data Quality / DRE Engineers to anchor DMF-based quality checks to model-level SLO definitions and data contract obligations; Leverage WinWire's WinAIDM accelerator platform for automated schema generation, source-to-target mapping scaffolding, and transformation layer bootstrapping

Client Engagement & Domain Collaboration Facilitate source-to-target mapping workshops with the customer's domain SMEs across Policy, Claims, Finance, and Actuarial workstreams - onshore proximity enables real-time decisions; Translate complex business data requirements from the customer's domain analysts and data stewards into validated, implementable logical models; Surface modeling trade-offs (denormalization vs. flexibility, performance vs. governance) as clear, decision-ready options for the Technical Architect and program stakeholders; Represent WinWire's modeling practice in customer-facing design review sessions and architecture steering committee presentations

What Success Looks Like/Expected outcome

• Logical and physical data models for all 9 Wave 1 certified data products reviewed, approved, and locked by Month 2 - zero schema rework required during Wave 1 delivery
• ACORD-based enterprise data model customization documented and adopted as the modeling standard across all delivery workstreams
• End-to-end Collibra lineage registered for 100% of Gold-layer modeled entities - attribute-level, not just table-level
• Data dictionary and source-to-target mapping documentation maintained as a current, version-controlled, and team-accessible living artifact
• All Wave 1 data products meet 99.9% completeness and 99.5% accuracy SLOs, with quality rules anchored to model-defined data contracts
• Customer domain SMEs describe the modeling approach as enterprise-grade, insurance-aware, and audit-traceable

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