Senior Data Engineer

About P&G

Procter & Gamble, established over 180 years ago, has evolved from a simple soap and candle maker into the world’s largest consumer goods company, with globally recognized brands enhancing daily life. With operations in around 70 countries, P&G values leadership, innovation, and corporate responsibility. The company is committed to diversity and equal opportunity for all employees, fostering an ethical and inclusive work environment.

Role Overview

We are seeking a Senior Data Engineer to join our Supply Chain Innovation team at the Hyderabad office. In this role, you will design and develop data infrastructure foundational to an AI-native operating environment where people and AI collaboratively enhance decision-making and business performance. This involves creating robust pipelines that feed knowledge and cognitive agents with trusted, real-time, governed data. Your work will encompass pipeline architecture, semantic layer design, KPI logic, and data acquisition strategies, utilizing Agile and DevOps methodologies in an international team setting.

Key Responsibilities

• Design comprehensive AI-native data ingestion, transformation, and serving pipelines optimized for analytic and agentic uses.
• Develop a semantic data layer to standardize schemas, metadata, and business definitions for reliable data interpretation by cognitive agents.
• Manage KPI factory layers by implementing back-end services encoding standard KPIs and business logic for consistent outputs.
• Define and maintain data acquisition strategies, including source systems, reference data, access controls, refresh cadence, and ownership.
• Build infrastructure to support agent retrieval, such as vector stores and embedding pipelines for production-scale knowledge agents.
• Implement governance frameworks for agent data access, including permissions, approval workflows, guardrails, and audit logging to uphold accountability.
• Oversee full application lifecycle management for AI-native data components deployed on Azure, including deployment, updates, and decommissioning.
• Apply engineering best practices including CI/CD, testing, data quality monitoring, drift detection, and evaluation of agent-facing data outputs.
• Collaborate across engineering, governance, and business teams to translate process redesigns into scalable data architectures.
• Design data products and access methods facilitating efficient human-AI collaboration, preserving judgment and control where necessary.

Candidate Profile

• Bachelor’s degree in a relevant technical discipline or equivalent practical experience.
• Minimum of 5 years’ experience developing Big Data solutions with Spark and Databricks, including AI or ML data product design.
• Deep understanding of modern Big Data frameworks such as Data Warehousing, Lakehouse, Data Mesh, and semantic layer architectures.
• Hands-on use of vector databases and embedding technologies (e.g., Azure AI Search, pgvector, FAISS) and design of retrieval-augmented generation pipelines.
• Proficiency in Python (with PySpark), SQL, plus familiarity with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex) and context engineering.
• Experience with Airflow and Azure Data Factory integrating agent orchestration and tooling APIs.
• Track record in designing data infrastructure including unit testing, CI/CD pipelines, APIs, and strong focus on data lineage, auditability, and governance for AI data.
• Knowledge of AI-native operating model principles and their application to data access and approval systems.
• Excellent interpersonal and communication skills enabling cross-functional collaboration.
• Commitment to delivering high-quality, reliable data infrastructure supported by best engineering practices and teamwork.