Senior Data Engineer
Procter & Gamble (company site, opens in a new tab)7+ yrsMumbaiOn-siteDirect career pageEst. 25–40 LPA
Posted on 21 Sept
Reach people at Procter & Gamble
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Procter & Gamble you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, and implement cloud-based data and analytics platforms and pipelines to acquire, cleanse, transform, and publish data from various sources.
- Ensure technical solutions align with architecture blueprints and provide data fit for business use, working closely with data asset managers and architects.
- Lead technical design discussions and drive solution decisions in cross-functional and global teams as a hands-on technical lead.
Minimum Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Technology, or related technical field, or equivalent experience.
- 7+ years of experience in data engineering with hands-on expertise in Big Data solutions using Spark and Databricks.
- At least 3 years of experience with cloud technologies such as Azure Cloud or Google Cloud, including software engineering and API development.
- Proven experience designing and developing end-to-end data solutions including architecture, coding, testing, deployment, and production support.
Ideal Candidate Profile
- Strong expertise in modern data platform architectures including Data Warehousing, Lakehouse, Data Mesh, and Data Quality.
- Proficient in Python (PySpark), SQL, and familiar with LLM orchestration frameworks and context engineering for data-facing agents.
- Experienced in CI/CD pipelines, API integrations, Agile and DevOps environments, and capable of leading technical design and collaboration across global teams.