J

J.Jill

Retailer of women's apparel and accessories

Lead Data Architect

Data ManagementPosted on Nov 5
Full-timeQuincy, MA, USAIn-person

About the Job

Requirements

  • A bachelor's degree in computer science, information technology, data management, statistics, mathematics, or a related field, or equivalent professional experience.
  • At least 10 years of experience in data architecture, engineering, management, stewardship, or related roles, including at least 5 years with enterprise data architecture, lakehouse platforms such as Databricks, Snowflake, or Microsoft Fabric, data modeling, SQL, governance, and data quality, with demonstrated technical leadership through architecture decisions, reviews, mentoring, stakeholder facilitation, and direct delivery.
  • At least 4 years of hands-on production Databricks experience at scale, including ownership of a medallion or equivalent lakehouse architecture from design through operation.
  • Strong PySpark and SQL skills, with practical experience in Delta Lake, Unity Catalog, workflow orchestration, production pipelines, and change data capture from relational sources such as Oracle and SQL Server.
  • Experience designing retail product repositories, customer data platforms, identity resolution, or master data management capabilities, and working with enterprise resource planning, ecommerce, customer, product, inventory, and transactional data.
  • Demonstrated knowledge of metadata, lineage, lifecycle management, privacy, and technical controls for access, consent, masking, retention, and deletion.

Responsibilities

  • Develop and maintain the enterprise data, analytics, and artificial intelligence architecture roadmap, including target-state patterns for integration, data analytics, migration, master data, and data lifecycle management across cloud and on-premises platforms.
  • Own 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, householding, customer history, and appropriate lakehouse-as-customer-data-platform 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 master data management platform and lakehouse so governed master data supports operational, analytics, and artificial intelligence 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 nonproduction refresh and masking strategies.
  • Define data contracts for merchandising 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 artificial intelligence and generative artificial intelligence patterns, including AI/BI Genie, retrieval-augmented generation, feature management, and model serving, and establish a roadmap for customer segmentation, propensity, churn, and lifetime value capabilities.
  • Govern artificial-intelligence-assisted development through approved tools, security and intellectual property controls, code-review standards, team enablement, and measured productivity and quality improvements.
  • Set architecture and engineering standards, lead design and code reviews, and make timely decisions on patterns, exceptions, technical debt, and production outcomes.
  • Mentor data engineers, marketing technology engineers, analysts, and other contributors; provide hands-on leadership for work requiring architecture depth.
  • Facilitate decisions with merchandising, planning, marketing, finance, digital commerce, supply chain, customer, Legal, and Compliance stakeholders, explaining technical concepts clearly to business audiences.

Desired Qualifications

  • An advanced Databricks, data management, governance, or data quality certification.
  • Retail, fashion, apparel, or direct-to-consumer experience across merchandising, inventory, order management, customer, marketing, and ecommerce data.
  • Experience integrating retail planning, ecommerce, customer data platform, and finance platforms with a lakehouse or enterprise data platform.
  • Hands-on experience with MLflow, feature stores, model serving, Azure data services, and commercial master data management, customer data platform, or product information management/product lifecycle management platforms.

About the Company

J.Jill

Retailer of women's apparel and accessories

J.Jill sells women's apparel, accessories, and footwear through both online and physical stores, with a focus on inclusive sizing that covers misses, petite, tall, and plus. Its products come in seasonal collections that are refreshed regularly to reflect current fashion trends. Customers shop through direct sales of clothing and accessories, and can use the brand’s own credit card, which offers benefits and discounts.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Tilton, NH
Founded
1959

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