Technical Product Owner_ Data & AI (Delhi)

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: Solid 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 1. Technical Empathy: Ability to understand technical constraints (performance, cost) and communicate effectively with data engineers. 2. Ambiguity Management: Comfortable working in complex environments with changing or unclear requirements. 3. 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. .