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 contemporary 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.

📌 Technical Product Owner_ Data & AI (Noida)
🏢 IRIS SOFTWARE
📍 Noida