KEY RESPONSIBILITIES
1. Product Lifecycle
Own the full lifecycle for internal and external data products, from discovery and design through launch, support, improvement, and retirement.
Work with business users to understand the problem, define the intended outcome, identify the right data, and agree on how success will be measured.
Set and maintain product vision, roadmaps, priorities, requirements, acceptance criteria, release plans, and product documentation.
Make sure each data product has a clear owner, purpose, user group, support model, quality expectations, metadata, lineage, refresh timing, known limits, and measurable business value.
Manage data products that may include enterprise datasets, data marts, master and reference data, governed metrics, reporting datasets, semantic models, APIs, and data used by AI/ML, analytics, and operational systems.
2. Product Delivery
Translate business needs into clear features, user stories, data rules, source-to-target needs, acceptance criteria, and delivery plans that engineering and analytics teams can execute.
Manage and prioritize the backlog based on business value, urgency, data availability, technical complexity, risk, platform cost, support impact, and team capacity.
Partner with data engineers, analysts, data scientists, architects, privacy, security, platform, and data governance teams to balance speed, quality, controls, cost, and long-term maintainability.
Work with engineering and architecture teams on practical product design choices, including batch or streaming patterns, Databricks medallion layers, reusable components, data contracts, semantic models, and downstream consumption patterns.
Identify dependencies, risks, data gaps, scope changes, access needs, platform constraints, and key decisions early so delivery stays on track and stakeholders understand tradeoffs.
Help users choose between an existing data product, self-service data, an AI-assisted answer, a managed report, or new development before adding new work to the backlog.
3. Adoption and Results
Track product performance, usage, reliability, data quality, refresh success, support demand, user feedback, and business outcomes.
Use KPIs, adoption trends, support tickets, data-quality results, and stakeholder feedback to decide what to improve, expand, simplify, automate, or retire.
Create clear documentation, definitions, lineage notes, training materials, release notes, and known-issue guidance so users can find, understand, and use available data products.
Drive adoption by explaining product value in plain language and helping business teams apply the data to real decisions and processes.
Partner with engineering and platform teams to make product health visible through monitoring, alerts, runbooks, service expectations, and permanent fixes for recurring issues.
Communicate status, risks, decisions, adoption, value, and delivery tradeoffs to leaders, business partners, and delivery teams.
4. Data Governance, Quality, and Controls
Make sure data products follow company standards for data quality, security, privacy, access, encryption, retention, stewardship, metadata, lineage, and responsible use.
Partner with governance, privacy, security, architecture, platform, and legal teams to manage data definitions, data lineage, access needs, classification, sensitive data controls, and compliance expectations.
Define quality checks, issue handling, ownership, service expectations, and escalation paths so users can trust the data and know how to get help.
Ensure data products include the controls needed for reliable production use, including testing, monitoring, documentation, release readiness, access approval, and support plans.
Support privacy and regulatory requirements, including appropriate controls for sensitive data, consumer data, and data used by AI-enabled tools or self-service analytics.
5. Workforce Planning and Forecasting
Forecast staffing and skill needs based on planned work, support demand, and team capacity.
Provide staffing and cost estimates for annual planning, project funding, and new work.
Recommend when to hire, use contractors, train employees, automate work, or move work between teams.
Track vacancies and expected staffing changes and explain how they affect delivery and funding.
6. AI Use and Team Expectations
Use approved AI tools in day-to-day work to prepare plans, analyze information, draft documentation, summarize issues, test ideas, and speed up routine tasks.
Set the expectation that employees use approved AI tools where those tools can reduce manual work, improve documentation, support data discovery, or shorten delivery time.
Help the team use tools such as Windsurf, Devin, and Databricks Genie for coding, testing, troubleshooting, documentation, requirements analysis, and data exploration when appropriate.
Guide the shift from custom report creation to governed self-service, reusable data products, and AI-assisted answers that business teams can use safely.
Require employees to review, validate, and test AI-generated work before it is used in production or shared as a final answer.
Make sure the team follows company rules for security, privacy, data handling, intellectual property, and responsible AI use.
Track where AI is helping the team and where training, controls, or process changes are needed.
7. People Management
Manage employees and set clear goals, priorities, and expectations.
Assign work, balance workloads, and make sure the team has the skills and support needed to deliver.
Give regular feedback, complete performance reviews, and address performance concerns.
Mentor and coach employees, build development plans, and prepare employees for larger roles.
Support hiring, onboarding, succession planning, and retention.
QUALIFICATIONS
Required
Bachelor’s degree in a related field, or equivalent experience.
8+ years of relevant experience, including experience managing data products, analytics products, data platforms, data warehouses, machine learning-enabled products, or similar technology products.
3+ years managing employees or technical teams, including planning work, setting priorities, coaching employees, and managing performance.
Experience translating business needs into requirements, user stories, data rules, acceptance criteria, delivery plans, product documentation, and release decisions.
Working knowledge of SQL and familiarity with Databricks, Spark, data pipelines, ETL/ELT, data modeling, semantic models, data quality, BI tools, and modern cloud data platforms.
Experience with Agile or Scrum ways of working, including backlog management, sprint planning, prioritization, stakeholder demos, release planning, and dependency management.
Experience applying data governance, privacy, security, access, metadata, lineage, and quality standards to data products.
Experience using approved AI tools to improve personal and team productivity while validating outputs before use.
Strong communication skills with the ability to explain data, analytics, product decisions, risks, costs, adoption, and tradeoffs to technical and non-technical audiences.
Experience with enterprise data products, master data, reference data, customer data, store or item data, analytics, BI, governed metrics, or self-service reporting.
Experience with tools such as Power BI, Tableau, Databricks, Azure cloud data platforms, data catalogs, data observability tools, data quality tools, Dataiku, or similar platforms.
Experience working with data science, machine learning, forecasting, AI-enabled products, conversational BI, or AI-assisted analytics.
Experience in retail, convenience, restaurant, supply chain, merchandising, finance, operations, loyalty, fuel, or digital commerce data domains.
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If an hourly or salary range is included in this ad it represents the range 7-Eleven in good faith believes is the range of compensation for this role at the time of this posting. The Company may ultimately pay more or less than the posted range. This range is only applicable for jobs to be performed in this state. This range may be modified in the future. No amount is considered to be wages or compensation until such amount is earned, vested, and determinable under the terms and conditions of the applicable policies and plans. The amount and availability of any bonus, commission, long-term incentive compensation, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
For a general description of all benefits 7-Eleven is offering in the US for the position, please visit this link.
For a general description of all benefits 7-Eleven is offering in Canada for the position, please visit this link.
