Senior Data Product Lead - Enterprise Analytics

7-Eleven, Inc

Irving (TX)

On-site

USD 140,000 - 200,000

Full time

8 days ago

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Job summary

7-Eleven, Inc is seeking a senior leader to own the lifecycle of data products from discovery to retirement. You will partner with data engineers, analysts, and governance teams to ensure measurable business value, quality, and security across enterprise data platforms.

You will drive adoption of governed self-service analytics, AI-assisted insights, and robust reporting via Databricks, Power BI, and SQL-based pipelines, while forecasting staffing and costs for ongoing initiatives.

Qualifications

Bachelor’s degree in a related field, or equivalent experience.
8+ years of relevant experience managing data products, analytics platforms, or data warehouses.
3+ years managing employees or technical teams.
Experience translating business needs into requirements, user stories, data rules, acceptance criteria, release plans and documentation.
SQL proficiency; familiarity with Databricks, Spark, ETL/ELT, data modeling and BI tools.
Experience with Agile/Scrum, backlog management, sprint planning and release planning.
Experience applying data governance, privacy, security, metadata, lineage, and quality standards to data products.
Experience using approved AI tools to improve productivity while validating outputs.
Strong communication skills to explain data decisions to technical and non-technical audiences.
Experience with enterprise data products, master/reference data, analytics, BI, or self-service reporting.
Experience with Power BI, Tableau, Databricks, Azure, data catalogs, data observability/data quality tools, or Dataiku.

Responsibilities

Own the full lifecycle for internal and external data products from discovery through retirement.
Translate business needs into features, user stories, data rules, and acceptance criteria for engineering and analytics teams.
Manage and prioritize backlog based on business value, urgency, data availability, and capacity.
Collaborate with data engineers, analysts, data scientists, architects, privacy, security, and governance teams.
Define practical product design choices for data pipelines, medallion layers, data contracts, semantic models, and downstream consumption.
Forecast staffing and costs; guide automation and AI usage to reduce manual effort and increase self-service.
Track product performance, adoption, data quality, and business outcomes; communicate status and risks to stakeholders.