As a Data Domain Architect, Senior Associate within the Workforce CoE and act as the accountable architect for the Workforce data domain, spanning HC, organizational structures, workforce movements, labor/benefits, and related expense measures. The role requires hands-on depth in Databricks and SQL , paired with strong domain architecture capabilities across canonical modeling , conformed dimensions , semantic layer enablement , and governance-by-design .
Success in this role depends on the ability to translate business outcomes into durable data assets with clear ownership and operational rigor. You will partner closely with Tech, Data, Finance, and other Product groups to define domain boundaries, standardize definitions and KPIs, reduce reconciliation effort, and increase trust in workforce data and reporting through measurable data quality, SLAs, and transparent lineage.
- Define and evolve the Workforce data domain architecture, including domain boundaries, canonical models, reference data, and conformed dimensions (e.g., worker, position, org, cost center, location, time), and design and maintain both logical and physical data models that support operational reporting and analytics.
- Lead the design and delivery of Workforce data products (curated, reusable datasets) aligned to the medallion architecture (bronze/silver/gold), including ingestion patterns, transformation standards, and consumption-ready structures, and define and enforce data contracts for upstream/downstream integrations (schema, semantic meaning, quality thresholds, refresh cadence, and change management expectations).
- Establish and operationalize data governance and controls, including stewardship workflows, metadata standards, lineage capture, and issue management, and define data quality rules (completeness, validity, timeliness, uniqueness, reconciliation controls), instrument monitoring, and drive remediation with producers and platform teams to meet documented SLAs.
- Partner with stakeholders to standardize Workforce definitions and measures (e.g., HC, FTE, vacancy, attrition, transfers, contingent labor, labor cost, benefits expense), document business rules, align curated data products to the semantic layer, and facilitate governed self-service consumption for dashboards, analytics, and regulatory/management reporting.
- Demonstrated experience in data domain architecture or closely related roles (data architecture, analytics engineering, domain data leadership) with a track record of delivering governed, reusable data assets as products.
- Strong hands-on capability in SQL for complex querying, profiling, reconciliation, and performance-aware design, including the ability to validate transformations and investigate data quality issues independently.
- Proficiency with Databricks in a lakehouse context, including designing curated datasets, working with Delta/managed tables, and applying domain modeling patterns that support scalable consumption.
- Strong understanding of financial data structures and hierarchies that will need to be modeled in Databricks, and ability to communicate well (written and verbal) with Finance stakeholders
- Strong competency in logical and physical data modeling, including dimensional modeling concepts (facts/dimensions), canonical models, and the use of conformed dimensions to drive cross-domain consistency.
- Practical experience implementing governance artifacts and operational controls, including metadata management, data lineage, stewardship engagement, auditability, and documented SLAs.
- Strong stakeholder management skills with the ability to translate business requirements into precise data definitions, drive alignment across Product/Finance/Technology, and communicate trade-offs clearly.
- Experience with workforce/HR and finance-adjacent datasets and the operational realities of headcount and labor expense reporting (e.g., effective-dated structures, hierarchies, retro changes, point-in-time vs. period measures).
- Familiarity with lakehouse patterns for incremental processing, reconciliation controls, and scalable curation practices.
- Exposure to data governance operating models (domain stewardship, issue triage, control evidence) and the implementation of enterprise metadata and cataloging practices.
- Experience supporting a semantic layer and BI consumption patterns where metric definitions must be consistent, versioned, and governed.