Lead Data Architect

At J.Jill, we’re redefining what it means to dress and live with ease. As a women-led, Boston-based lifestyle brand with 200+ stores nationwide, we design thoughtful, inspired apparel and accessories that celebrate the totality of all women. We’re entering an exciting chapter of growth — expanding our footprint, reaching new customers, and deepening long-standing relationships. We're seeking bold thinkers and doers to join us.

With competitive pay and benefits, a supportive culture, and the chance to make a real impact, this is more than a job, it’s an opportunity to bring our ethos keep it simple, make it matter to life in new and inspiring ways.

J. Jill is strengthening its data, analytics, and master data management capabilities to support consistent enterprise reporting, better business decisions, and artificial intelligence. The Lead Data Architect owns the technical architecture for the Databricks Lakehouse, master data management, data governance, and the AI capabilities built on that foundation.

• Develop and maintain the enterprise data, analytics, and AI architecture roadmap, including target-state patterns for integration, data analytics, migration, master data, and data lifecycle management across cloud and on-premises platforms.
• Own the end-to-end Databricks Lakehouse architecture, including medallion layers, Delta Lake, Unity Catalog, workspace and cluster policies, orchestration, role-based access, observability, recovery, and cost management.
• Design and build material components using PySpark and SQL; establish reusable patterns for ingestion, change data capture, transformation, data products, schema evolution, testing, and deployment.
• Define authoritative sources, ownership, publishing patterns, and downstream responsibilities for customer, product, inventory, vendor, location, and transactional data.

• Architect enterprise master data to consolidate data sources, field definitions and values, repository and distribution strategy, customer identity resolution, golden records, house holding, customer history, and appropriate lakehouse-as-CDP patterns.
• Own master data architecture for item, customer, vendor, and location, including canonical models, match and merge logic, survivorship, hierarchy management, and system-of-record decisions.
• Define the interface between the MDM platform and lakehouse so governed master data supports operational, analytics, and AI needs without duplicating business rules.

• Lead the enterprise data governance and stewardship forum with business data owners, Legal, Compliance, Security, and technology teams; define ownership, decision rights, approvals, and escalation paths.
• Maintain a business glossary and metadata catalog covering definitions, schemas, lineage, ownership, classification, and approved uses, including data held or processed by third parties.
• Establish measurable standards for data accuracy, completeness, consistency, timeliness, uniqueness, validity, reconciliation, and observability; route issues to accountable owners and address root causes.
• Ensure shared business intelligence measures use governed sources and consistent calculations.

• Define data classification, access, encryption, masking, consent, retention, and handling controls based on sensitivity, business value, and applicable requirements.
• Partner with Legal and Compliance to translate CCPA, CPRA, GDPR, audit, and retention requirements into verifiable technical controls, including Unity Catalog policies and right-to-delete workflows.
• Coordinate data creation, maintenance, archival, and deletion across production and third-party systems, and define compliant non-production refresh and masking strategies.

• Define data contracts for Merch planning and allocation systems, ecommerce, product lifecycle management, finance, customer platforms, and other strategic consumers, including grain, ownership, cadence, quality, security, reconciliation, and change management.
• Design governed return paths for forecasts, recommendations, segments, and other derived outputs from the lakehouse to operational systems.
• Define approved Databricks AI and GenAI patterns, including AI/BI Genie, retrieval-augmented generation, feature management, and model serving, and establish a roadmap for customer…