Manager Data Engineering bei Williams Sonoma Inc.

Role: Manager, Technology - Data, Analytics & AI
Summary

In this exciting role, you would:
 

• Own the health, performance, and growth of a multidisciplinary Data, Analytics & AI domains and Engineering team 
(of Data/BI/ML Engineers and Analysts). 
• Create the conditions for sustainable high performance: clear direction, strong collaboration, and career 
development. 
• Enable the team to turn business problems into robust solutions and actionable insights while maturing practices, 
processes, and talent.
Key Responsibilities
• Manage, coach, and develop a high-performing team (hiring, goal setting, feedback, growth planning).
• Partner with Tech Leadership, Product, and Business stakeholders to convert requirements into scalable data 
models, pipelines, semantic layers, and dashboards.
• Participate in the development of the technical vision for your team including contributing to the long-term roadmap.
• Drive data quality, governance, documentation, lineage, and metric definitions; reduce redundancy.
• Set and enforce engineering and analytics best practices (coding standards, version control, peer review, CI/CD, 
automated testing) while building an outcome/impact-based culture.
• Escalate and resolve complex technical and data issues; remove delivery blockers.
• Closely monitor status, identify and predict risks. Provide meaningful and actionable insights on progress.
• Manage project forecast and spending.
 

REQUIREMENTS AND QUALIFICATIONS
 

• 8+ years in data engineering / BI / analytics, including 3+ years directly managing teams. 2+ years of working 
experience with ML & Data Science is a plus.
• Bachelor’s in Computer Science/Applications, Engineering, Information Technology/Systems, or equivalent 
experience. Masters in any of these areas is a plus.
• Proven delivery of scalable data platforms and BI solutions in cloud (Azure preferred).
• Expertise with SQL, ELT/ETL patterns, dimensional & semantic modeling.
• Experience with Snowflake, Databricks, Data Factory, Oracle.
• Working knowledge of Python, Spark, Scala, and streaming technologies a huge plus.
• Strong BI development (Power BI and/or MicroStrategy) and performance tuning.
• Demonstrated governance: data quality frameworks, metadata, metric standardization.
• Solid grasp of software engineering practices (testing, code review, automation, DevOps).
• Excellent stakeholder management; able to translate between business and technical contexts.
• Track record of talent development and building collaborative, inclusive teams.
• Clear written and verbal communication; executive presentation capability.
 

Typical Success Metrics
 

• Team Health: Low regrettable attrition.
• Delivery Predictability: ≥80% of committed quarterly objectives delivered without quality regressions.
• Talent Growth: Documented progression for each team member; internal promotions / expanded scope.
• Operational Maturity: Reduction in unplanned urgent work and data incident frequency; improved cycle time.
• Stakeholder Trust: Positive satisfaction feedback (clarity, responsiveness, value realized).
• Adoption & Impact: Growth in usage of certified data products/dashboards and measurable business decisions 
influenced

Company