Role Summary We are looking for a hands-on Data Modelling Technical Lead to own the target-state logical and physical data model for a Fortune 500 enterprise s multi-wave, enterprise-scale data modernization program. You will design and govern Snowflake-native dimensional and relational models across 9+ certified data products spanning Policy, Claims, Finance, Actuarial, and MDM domains and establish the ACORD-based modeling standards that the full delivery team will build on. Working directly alongside the onshore Technical Architect, you will be the authoritative voice on schema quality, naming conventions, and Collibra-aligned lineage definitions throughout this multi-year engagement.
Why This Role Matters The foundation of every certified data product in this program is the data model get it right and 27 engineers build on a stable, reusable, lineage-ready schema; get it wrong and technical debt compounds across three waves and 80+ source systems. This engagement migrates 372 production database instances into a unified Snowflake cloud-native platform, and the customer s ACORD-based enterprise model must accommodate insurance domain complexity at scale while remaining dbt-compatible and Collibra-governed. The onshore placement is intentional direct, synchronous engagement with the customer s domain SMEs is how modeling decisions get made fast and right.
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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