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