Staff Product Manager, Data Platform

Overview

Intuit is a global technology platform dedicated to helping customers and communities overcome significant financial challenges. We empower over 50 million consumers, small businesses, and self-employed individuals globally to prosper.

The Trust and Safety Platform product management team is responsible for driving foundational capabilities and methodologies that profoundly protect Intuit customers against risk and fraud. This critical work demands a leader who can effectively leverage data to amplify our impact.

We are seeking a Staff Product Manager, Data Platform to strategically define and execute our data ecosystem vision, with a strong focus on enabling self-service data consumption across the organization.

In this role, you will bridge the gap between Data and Risk analysts, Data Engineering, Data Science, and various business units, ensuring that our data assets are of high quality, discoverable, and packaged as successful products that deliver measurable business value.

This position offers a unique opportunity to shape the core data infrastructure powering Intuit's next generation of products, working with cutting-edge technology and a team committed to innovation.

Responsibilities

• Product Strategy and Vision: Define and champion the product vision and roadmap for our data products, which include internal analytics platforms, external data APIs, machine learning model outputs, and core data pipelines.
• Stakeholder Collaboration: Serve as the primary liaison between data scientists, data engineers, software engineers, and business stakeholders to gather requirements, prioritize features, and manage expectations effectively.
• Requirements Definition: Translate complex business problems and user needs into clear, actionable user stories, functional specifications, and acceptance criteria for various data products.
• Self-Service Enablement: Design and optimize data products to maximize self-service consumption. This involves curating accessible datasets, developing intuitive interfaces, and ensuring data assets are well-documented and easily discoverable by business users.
• Data Tool Ownership: Drive the adoption, feature roadmap, and overall success of our core data services, empowering analysts and business users with direct data access.
• Prioritization and Roadmap Management: Manage the backlog for data product development, making tough trade-off decisions based on business impact, technical feasibility, and strategic alignment.
• Product Launch and Adoption: Plan and execute successful product launches, including developing go-to-market strategies, comprehensive documentation, and driving widespread adoption of data products across the organization.
• Performance Measurement: Define, track, and analyze key performance indicators (KPIs) for data products, such as data quality, latency, usage, business impact, and cost, to ensure product success and inform future iterations.

Qualifications

• 5+ years of product management experience, with at least 3 years specifically managing data products, data platforms, or analytics tools.
• 2+ years of hands-on experience with Intermediate SQL and strong analytics skills.
• Understanding of NoSQL methodology, data modeling, and machine learning models.
• Demonstrated expertise in Self-Service tools; a proven ability to increase data literacy and drive tool adoption within an organization.
• Strong technical fluency in the data domain, including a working understanding of:
• Experience in public cloud service providers (AWS, Azure, GCP).
• Data warehousing tools (e.g., Snowflake, BigQuery, Redshift).
• Big Data Processing Frameworks (e.g., Apache Spark, DataBricks).
• ETL/ELT processes and data pipelines (e.g., Airflow, DBT).
• Data governance, quality, and security principles.
• Understanding of streaming architecture.
• Fluency in GDPR / CCPA / 7216 restrictions and regulations.

Key skills/competency

• Product Management
• Data Platform
• Data Strategy
• Self-Service Enablement
• Data Governance
• SQL
• Cloud Platforms (AWS, Azure, GCP)
• Data Warehousing (Snowflake, BigQuery)
• Big Data Processing (Apache Spark)
• ETL/ELT (Airflow, DBT)