Job Role :Senior Data Engineer (AI Enablement Focus) Location**: Bangalore

ABOUT UNILEVER With 3.4 billion people in over 190 countries using our products every day, Unilever is a business that makes a real impact on the world. Work on brands that are loved and improve the lives of our consumers and the communities around us. We are driven by our purpose: to make sustainable living commonplace, and it is our belief that doing business the right way drives superior performance. At the heart of what we do is our people - we believe that when our people work with purpose, we will create a better business and a better world. At Unilever, your career will be a unique journey, grounded in our inclusive, collaborative, and versatile working environment. We don’t believe in the ‘one size fits all’ approach and instead we will equip you with the tools you need to shape your own future.

ABOUT GDT : GDT is the global digital and technology engine of Unilever offering business services, technology, and enterprise solutions. GDT serves over 190 locations and through a network of specialized service lines and partners delivers insights and innovations, user experiences and end-to-end seamless delivery making Unilever Purpose Led and Future Fit.

About Us : Wild are on a mission to remove single-use plastic from the bathroom, armed with our refillable, natural and scent-sational deodorants, body wash, and lip balm - and we’re only just getting started. We launched in 2020 and as a high-growth company we’re already one of Europe’s fastest growing start-ups. Role Summary

Autonomous agents are only as good as the data they can reason over and act on. The Data Engineer (AI Enablement Focus) builds and maintains the semantic data layer that makes Wild's agents accurate, grounded,



and trustworthy — with Snowflake as the core data source, optimised specifically for AI consumption.

This is a specialised data engineering role aimed at the AI use case rather than traditional BI. You will design pipelines and data products that feed retrieval and agent workflows, prepare and structure data for embeddings/RAG, and ensure freshness, quality, and lineage. You are the bridge between Wild's raw data estate and the agents that depend on it.

You will also be a guardian of data governance: ensuring access controls, privacy, and quality standards are met so that agents can be deployed safely at scale. Your work directly determines how reliable and explainable the agents can be. - Key Responsibilities

- Optimise Snowflake for AI consumption: semantic modelling, performance tuning, and structuring data for retrieval and agent use.
- Build and maintain data pipelines (batch and near-real-time) that supply agents with fresh, reliable data.
- Prepare data for RAG: chunking, embeddings, vector storage, and metadata for grounded retrieval.
- Create reusable data products and curated datasets for agent workflows.
- Implement data quality, validation, and lineage so agent outputs are trustworthy and auditable.
- Enforce data governance: access control, privacy/PII handling, and compliance in partnership with governance teams.
- Integrate Snowflake with GCP services and the agent platform securely and efficiently.




- Monitor and tune pipeline cost and performance alongside FinOps.

Required Skills & Experience

- 10+ years of overall experience
- 4+ years in data engineering, with strong Snowflake expertise (modelling, performance, security).
- Advanced SQL and advanced proficiency in Python for data pipelines.
- Experience with the modern data stack and pipeline (dbt, fivetran etc).
- Experience building production data pipelines and orchestration (e.g. dbt, Airflow, or equivalent).
- Working knowledge of GCP data services and Snowflake-cloud integration.
- Understanding of RAG data preparation: embeddings, vector stores, chunking, and metadata.
- Strong grasp of data quality, lineage, and governance practices.
- Awareness of privacy/security requirements for enterprise data. - Preferred Qualifications
- Experience enabling AI/ML or LLM workloads specifically (feature/retrieval pipelines).
- Snowflake and/or GCP certifications.
- Familiarity with vector databases and semantic search.
- Bachelor's in Computer Science, Data Engineering, or a related field. - Key Success Metrics
- Data freshness and pipeline reliability (e.g. 99% on-time, monitored SLAs).
- Retrieval quality: measurable improvement in agent grounding/accuracy attributable to data products.
- Coverage: percentage of priority agent use cases served by governed, reusable datasets.
- Data quality: reduction in data-related agent errors and rework.
- Governance: 100% of AI-consumed data covered by access controls and lineage.
- Cost: Snowflake/pipeline cost per workload optimised quarter-on-quarter. - Collaboration & Stakeholders
- Lead Manager - AI Platform & Engineering (architecture alignment).
- AI Developer (data access patterns for agents).
- F

📌 Sr. Data Engineer (Ai Enablement Focus) (Bengaluru)
🏢 Unilever
📍 Bengaluru