Data Engineer (Databricks)

Overview

Hands-on Data Engineer (Databricks) on the Trade Promotion & Analytics team, building a unified data foundation across enterprise systems and acting as technical SME and reviewer for external vendor developers.

What you'll do

• Design, build, test and deploy automated data pipelines and ETL/ELT processes on the Azure and Databricks platform.
• Write clean, maintainable, production-ready Python and PySpark code for data transformations, integrations and aggregations.
• Develop, tune and optimize complex SQL queries to ensure high performance and efficient pipeline execution.
• Ensure end-to-end data quality, consistency and reliability across all production data flows.
• Contribute to designing and implementing a harmonized relational and analytical data model as the single source of truth for downstream analytics.
• Analyze existing multi-source data models and schemas to understand data flows, entity relationships, constraints and business logic.
• Collaborate with Data Analysts and business stakeholders to translate analytical requirements into robust, scalable data structures.
• Ensure data modeling consistency, best practices and thorough architectural documentation across data assets.
• Act as technical point of contact, SME and quality gatekeeper for deliverables developed by external vendor engineers.
• Perform structured code reviews and pull request approvals to ensure compliance with P&G coding, testing and security standards.
• Partner with the Product Owner to track delivery milestones, technical trade-offs and early mitigation of technical debt.
• Promote best engineering practices, CI/CD and shared team standards.

What you'll need

• Demonstrated experience conducting code reviews, establishing best practices and providing technical guidance to junior peers or external vendor developers.
• Strong communication skills with the ability to articulate technical trade-offs to project stakeholders.
• Expert-level understanding and hands-on experience with relational data modeling, data warehousing concepts (e.g., star/snowflake schemas) and database design.
• Expert-level proficiency in SQL, including complex queries, performance tuning and advanced functions across different database systems.
• Advanced programming skills in Python for data manipulation (e.g., Pandas).
• Proven, hands-on experience developing solutions with PySpark in a distributed computing environment.
• Strong, practical experience with the Azure data stack (e.g., Azure Data Lake Storage, Azure Data Factory, Synapse Analytics) and the Databricks platform.
• Exceptional analytical, problem-solving and critical-thinking skills with a demonstrated ability to deconstruct complex business and data problems.

Details

• Location: Hyderabad, India (Hyderabad Office).
• Work mode: hybrid model, with the option to work from home two days a week.
• Employment type: full time.