D2C Data Product Analyst

Job Description

Job Description

The Digital Data Product Analyst serves as a critical bridge between business stakeholders, analytics teams, and data management groups. This role combines strong data analysis capabilities with core business analysis skills to ensure that data is effectively leveraged to inform strategic decision making, improve processes, and influence solution design. In addition to championing data stewardship, the Data Product Analyst plays a key role in eliciting requirements, documenting business processes, and facilitating alignment across technical and business teams.

Responsibilities

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• Act as the Data Subject Matter Expert to stakeholders with an understanding of data across the end‑to‑end data supply chain
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• Collect, clean, and analyze data from multiple sources to uncover trends, patterns, and actionable insights.
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• Translate analytical findings into clear, business‑friendly insights that support decision‑making.
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• Elicit, document, and manage business, functional, and non‑functional requirements through techniques such as interviews, workshops, process analysis, and user story creation.
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• Conduct stakeholder analysis to understand needs, roles, dependencies, and expectations across teams.
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• Support prioritization by assessing business value, impacts, complexity, and risk.
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• Ensure traceability of requirements from intake through development, testing, and deployment.
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• Collaborate with data engineering analysts, architects, and engineers to ensure solutions reflect business context and use cases.
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• Define testing acceptance criteria, support user validation, and provide structured feedback for continuous improvement.
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• Monitor and enhance data quality through profiling, validation, remediation, and alignment with organizational priorities.
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Qualifications

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• Bachelor's degree in information systems, business analysis, or related fields.
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• 5+ years of experience in data analysis, business analysis, product analytics, or similar roles.
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• Experience working in digital and D2C environments with exposure to clickstream, customer engagement, and media analytics data, including tools such as Adobe Analytics and Google Analytics.
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• Proven ability to work effectively in cross‑functional environments and support the delivery of data products that serve multiple stakeholders.
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• Skilled at requirements elicitation, process mapping, and translating business needs into technical specifications.
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• Skilled in crafting user stories, acceptance criteria, and high‑quality product and data documentation to support scalable, repeatable workflows.
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• Strong understanding of data stewardship principles, including data quality, lineage, metadata management, and responsible data use and the ability to adapt analytical and stewardship skills across a wide range of client use cases.
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• Comfortable connecting data across multiple client systems and enriching datasets to enable new insights for sports, fan, and business stakeholders.
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• Excellent communication, with the ability to tailor messages for both technical teams (engineering, data science) and business stakeholders (club personnel, league departments).
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• Champions a consumer-first mindset, consistently grounding product, data and insights decisions with a deep understanding of consumer behaviors and needs.
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• Collaborative mindset with the ability to influence without authority and drive alignment across cross‑functional teams.
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• Skilled at navigating ambiguity, asking the right clarifying questions, and moving teams toward clarity and action.
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• Naturally curious, with a passion for exploring new data sources, uncovering insights, and improving data quality.
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• Strong critical thinking and structured problem‑solving, especially when interpreting incomplete, messy, or evolving datasets.
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• Strong analytical toolkit leveraging SQL, Python, and data querying tools to explore, transform, and validate complex datasets that span across multiple product disciplines.
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• Working knowledge of data modeling, ETL/ELT pipelines, and cloud data platforms (e.g., Snowflake, Databricks, AWS) to support reliable data products.
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