Technical Product Owner - Senior Engineer

Iris Software; Dadri, Gurugram District; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Iris Software in India is seeking a Technical Product Owner - Senior Engineer to lead data product strategy and the Databricks Lakehouse roadmap. You will collaborate with Marketing, Finance, and Engineering to translate requirements into actionable data tasks and ensure data quality and security.

The role emphasizes data governance, SQL/Python skills, and cloud data services, with Agile practices driving delivery timelines.

Qualifications

• 5+ years of experience as a Product Owner or similar in data-driven environments.
• Hands-on Databricks SQL, Notebooks, and workflows experience.
• Strong understanding of modern data architectures (Medallion, Lakehouse, ELT).
• Proficiency in SQL and familiarity with Python/Spark.
• Experience with cloud data services (Azure Data Factory, ADLS, AWS/GCP).
• Familiar with Jira, Confluence, and other Agile tools.

Responsibilities

• Own, prioritize, and maintain the data product backlog based on value and ROI.
• Define and execute the Databricks Lakehouse roadmap and data pipelines.
• Bridge business units and engineering by translating requirements into tasks.
• Deliver end-to-end data products with focus on quality and security.
• Partner on data governance, security, and quality controls in the Lakehouse.
• Facilitate Agile ceremonies to support high-velocity delivery.

Skills

Product Owner

Databricks

Data Architecture

SQL

Python/Spark

Cloud Platforms

Agile Tools

Education

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field

Tools

Jira

Confluence

Databricks Notebooks

Databricks Workflows

Job description

Job Description

Technical Product Owner - Senior Engineer

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

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.

Mandatory Competencies

• Big Data - Big Data - Pyspark
• Cloud - Cloud - Snowflake
• Data Science and Machine Learning - Data Science and Machine Learning - Databricks
• Data AI - Data Engineering - Apache Kafka
• Programming Language - Python - Apache Airflow
• Database - Database Programming - SQL
• Data Science and Machine Learning - Data Science and Machine Learning - Apache Spark
• Data AI - Data Engineering - Data Quality Validation
• Beh - Communication and collaboration