Senior Data Product Lead 5284

Job Title: Senior Data Product Lead Location: Hybrid – Atlanta Contract: 6–12 Months+ Overview We are seeking a senior level, hands-on Senior Data Product Lead to own the end-to-end lifecycle of high value data products in a fast-moving, evolving environment. This role requires a highly autonomous operator who can function as a “principled entrepreneur”—someone who treats data products like business assets, proactively drives adoption, and ensures delivery from concept through production. This is a contract role (6–12 months) supporting a newly forming data organization, where the permanent senior leadership structure is not yet in place. The contractor will play a critical role in stabilizing and accelerating early stage data product development. Key Responsibilities • Partner directly with business stakeholders (RevOps, Product, Customer Success) to understand and translate use cases into data products • Own the full data product lifecycle from system of record → Snowflake data mart → end-user consumption tools • Direct Data Engineering teams to deliver scalable, production-ready data solutions • Perform hands-on validation and unit testing to ensure data accuracy, logic integrity, and business alignment • Identify gaps, inconsistencies, and opportunities across data pipelines and proactively resolve them • Ensure data products are usable, trusted, and adopted by end users • Operate independently in a high-ambiguity environment with minimal oversight Required Experience & Skills • Strong hands-on experience with Snowflake • Proven experience with: • Snowflake Marketplace (publishing, consuming, or operationalizing data products) • Medallion architecture (bronze, silver, gold layer design and implementation) • Snowflake Cortex (AI-enabled data use cases, LLM/data interaction) • Snowflake Streamlit (data applications and dashboards) • Strong understanding of data product lifecycle and operational data delivery • Ability to work directly with Data Engineering teams and guide execution without formal management authority • Hands-on technical capability (SQL, data validation, modeling as needed) • Strong communication skills with both technical and business stakeholders Nice to Have • Experience with ML feature stores and downstream AI/ML consumption layers • Experience operating in RevOps, Product, Customer Success, or cross-functional data environments • Background in startup, high-growth, or transformation-stage organizations Ideal Candidate Profile The ideal candidate is a hybrid of: • Data product leader • Hands-on technical operator • Business-minded problem solver They are comfortable working without structure, thrive in ambiguity, and naturally think in terms of: • Adoption • Business impact • Data usability • End-to-end ownership