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Lululemon Data Analytics Jobs (NOW HIRING)

Huckberry

Data & Analytics Manager

Austin, TX · On-site

Architect and implement AI-powered analysis workflows activating our data in LLMs and Hex to enable ... like Lululemon, Tacoma, and Coors. We look forward to meeting you. Want to get to know us better?

Huckberry

Data & Analytics Manager

Austin, TX · On-site

Architect and implement AI-powered analysis workflows activating our data in LLMs and Hex to enable ... like Lululemon, Tacoma, and Coors. We look forward to meeting you. Want to get to know us better?

Huckberry

Data & Analytics Manager

Austin, TX · On-site +1

Architect and implement AI-powered analysis workflows activating our data in LLMs and Hex to enable ... like Lululemon, Tacoma, and Coors. We look forward to meeting you. Want to get to know us better?

Lululemon

Applied AI/ML Scientist

Seattle, WA · On-site

The team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon's vertically integrated retail ecosystem, embedding ...

lululemon

Applied AI/ML Scientist

Seattle, WA

The team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon's vertically integrated retail ecosystem, embedding ...

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Lululemon Data Analytics information

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$33K

$81.5K

$140K

How much do lululemon data analytics jobs pay per year?

As of Jun 7, 2026, the average yearly pay for lululemon data analytics in the United States is $81,518.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $96,500.00 per year, depending on experience, location, and employer.

What is the difference between Lululemon Data Analytics vs Lululemon Business Intelligence?

AspectLululemon Data AnalyticsLululemon Business Intelligence
Primary FocusAnalyzing data to identify trends and support decision-makingDeveloping and managing BI tools and dashboards for strategic insights
Skills & CertificationsData analysis, SQL, Excel, statistical toolsData visualization, BI platforms (e.g., Tableau, Power BI), SQL
Work EnvironmentCollaborative teams, data-driven projectsCross-departmental, strategic planning focus
Industry UsageCommon in retail and apparel companies

While both roles involve working with data within Lululemon, Data Analytics focuses on analyzing raw data to uncover insights, whereas Business Intelligence emphasizes creating tools and dashboards for ongoing strategic decision-making. Both roles are essential for leveraging data to drive business growth.

What cities are hiring for Lululemon Data Analytics jobs? Cities with the most Lululemon Data Analytics job openings:
Infographic showing various Lululemon Data Analytics job openings in the United States as of May 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 75% In-person, and 25% Remote job distribution, with an average salary of $81,518 per year, or $39.2 per hour.

Data Engineer - Data Engineering

Futran Tech Solutions Pvt. Ltd.

Columbus, OH • On-site

$110K - $132K/yr

Full-time

Posted 27 days ago

Job description

Client - Lululemon
Data Engineer - Data Engineering
Location: Columbus, Ohio (On-site / Hybrid)
Role Type: Individual Contributor
Role Summary We are seeking a skilled Data Engineer with 5 years of hands-on experience building and maintaining robust data pipelines, data lakes, and analytical platforms. Based in Ohio, this is an individual contributor role with direct engagement with business stakeholders across lululemon's Ohio-based operations. The successful candidate will own end-to-end data engineering deliverables independently, translating business requirements into scalable, production-grade data solutions that power analytics and AI/ML workloads.
Key Responsibilities
  • Design, build, and maintain scalable ETL/ELT pipelines using Python, Apache Spark, and Azure Data Factory to ingest data from diverse retail and operational sources into a centralised data lake (Microsoft Fabric / OneLake)
  • Engage directly with Ohio-based business teams (supply chain, store operations, finance, and merchandising) to gather data requirements, understand domain logic, and translate business needs into well-defined data models and pipeline specifications
  • Independently own the full data engineering lifecycle for assigned domains - from requirements gathering and data modelling through to pipeline deployment, monitoring, and ongoing optimisation
  • Build and manage Bronze, Silver, and Gold data layers in the lakehouse architecture, applying data quality checks, schema validation, and partitioning strategies to ensure reliable, performant datasets for downstream analytics and ML teams
  • Participate actively in agile ceremonies (sprint planning, stand-ups, retrospectives), self-manage delivery against sprint commitments, and proactively surface risks or blockers without requiring escalation
  • Implement and enforce data quality frameworks, lineage tracking, and cataloguing standards using Microsoft Purview, ensuring datasets meet governance and compliance requirements (GDPR, CCPA)
  • Support and contribute to Global Fulfillment and supply chain data initiatives, acting as the primary data engineering liaison for Ohio-based operational teams and ensuring timely delivery of data products that enable real-time decision-making
  • Stay current with emerging data engineering tools, patterns (e.g. data mesh, streaming architectures), and Microsoft Fabric capabilities; apply relevant advancements to continuously improve the data platform
    Qualifications
  • 5+ years of hands-on experience as a Data Engineer, with a proven track record of independently delivering production-grade data pipelines and data products in a cloud-based environment
  • Proficiency in Python and SQL for data transformation, with hands-on experience using Apache Spark (PySpark) for large-scale batch and streaming data processing
  • Solid understanding of data modelling concepts (dimensional modelling, star/snowflake schema, data vault) and experience building lakehouse architectures with Delta Lake or Apache Iceberg
  • Demonstrated ability to work directly with non-technical business stakeholders - gathering requirements, explaining data concepts in plain language, and iterating quickly on feedback to deliver business value
  • Experience with version control (Git), CI/CD pipelines, and DataOps practices - including automated testing of data pipelines and Infrastructure-as-Code (Terraform or Bicep)
  • Familiarity with data governance frameworks, data cataloguing (Microsoft Purview or Apache Atlas), and implementing data quality rules and observability monitoring within pipelines
  • Strong analytical mindset, attention to detail, and self-starter attitude - comfortable driving work forward independently in a fast-paced retail technology environment with minimal day-to-day supervision

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