Technical Product Owner_ Data & AI

Job Title: Technical Product Owner Data & AI (Databricks) Job Summary We are seeking a seasoned Technical Product Owner to drive the vision, roadmap, and delivery of our data analytics and AI/ML products. You will lead a couple of squads within our data organization, translating complex business needs into technical user stories, specifically focusing on building scalable, well-governed data services using Databricks on cloud infrastructure (Azure/AWS/GCP). Key Responsibilities Backlog Ownership: Own, prioritize, and maintain the data product backlog (user stories, epics) based on business value, technical necessity, and ROI. Databricks Strategy: Define and execute the product roadmap for Databricks Lakehouse adoption, including data pipelines (ETL/ELT), Delta Lake optimization, and SQL analytics. Stakeholder Collaboration: Act as the key interface between business units (Marketing, Finance, etc.) and engineering, translating requirements into actionable technical tasks. Delivery Management: Drive end-to-end delivery of data products, including data quality, security, and performance testing before production release. Data Governance & Quality: Partner with data stewards to ensure data governance, security, and quality controls are implemented within the Data Lakehouse. Agile Leadership: Facilitate Agile ceremonies (sprint planning, backlog grooming, daily stand-ups) to ensure high-velocity delivery. Required Skills & Experience Experience: 5+ years of experience as a Product Owner, Technical Product Owner, or Technical Product Manager in data-driven environments. Databricks Expertise: Hands-on experience with Databricks SQL, Notebooks, and workflows. Technical Knowledge: Strong understanding of modern data architectures (Medallion architecture, Data Lakehouse, ELT processes). Data Languages: Proficiency in SQL and familiarity with Python/Spark for data manipulation. Cloud Platforms: Proven experience working with cloud data services (Azure Data Factory, ADLS, or AWS/GCP equivalents). Agile Tools: Experience with Jira, Confluence, or similar Agile project management tools. Nice to Have Experience with Machine Learning (MLflow) and Data Science use cases in Databricks. Experience with Data Governance tools (Unity Catalog). Familiarity with DevOps practices (CI/CD) in data engineering. Key Competencies for Success Technical Empathy: Ability to understand technical constraints (performance, cost) and communicate effectively with data engineers. Ambiguity Management: Comfortable working in complex environments with changing or unclear requirements. Data Democratization: Passionate about making data easily consumable for business users. Typical Educational Requirements Bachelors degree in computer science, Data Engineering, Information Systems, or a related field.