Designation: Manager - Data Engineering

Experience: 10 to 14 Years

Location: Bengaluru

:

We need a senior data engineering resource who is super deep into building the infrastructure layer, has expertise in the Databricks stack and thinks AI first in terms building out the infrastructure - We are looking for this person as a Senior leader for the customer enablement stack who can support and help us get to the next level while building a truly AI centric stack for us.

Responsibilities

- AI-Centric Infrastructure Design
- Architecting the Lakehouse: Lead the design and implementation of a robust Medallion Architecture (Bronze/Silver/Gold) specifically optimized for downstream machine learning and LLM consumption.
- Vector Database Integration: Architect the seamless integration of Databricks Vector Search and managed vector databases to support RAG-based applications.
- Model-Ready Pipelines: Build 'feature-first' data pipelines where data is versioned, lineage-tracked, and ready for training without manual preprocessing.

- Databricks Stack Mastery

- Unity Catalog Governance: Implement enterprise-wide data governance, security, and discovery using Unity Catalog to ensure AI models access data ethically and securely.
- Delta Live Tables (DLT): Deploy and manage complex, streaming data pipelines using DLT to reduce operational overhead and increase data reliability.
- Compute Optimization:



Manage and optimize Serverless SQL warehouses and automated cluster scaling to balance high performance with cost-efficiency.

- Customer Enablement Scalability

- Internal Productization: Treat the data stack as a product, building self-service tooling that allows internal "customers" (DS/ML teams) to spin up environments and access clean data instantly.
- Performance Engineering: Debug and resolve deep-seated architectural bottlenecks in Spark jobs to ensure sub-second latency for customer-facing AI features.
- Technical Evangelism: Act as the bridge between core engineering and customer-facing teams, translating complex infrastructure capabilities into business value.

- AI Operations (DataOps MLOps)

- CI/CD for Data: Establish rigorous CI/CD practices for infrastructure-as-code (Terraform/Pulumi) and data pipeline deployments.
- Monitoring Observability: Implement advanced monitoring for data quality (Excellent Expectations/Monte Carlo) and model drift, ensuring the AI stack is "self-healing".
- Agentic Framework Support: Design the backend infra to support LangChain or LlamaIndex workflows, ensuring the data retrieval layer is fast enough for agentic reasoning.

Skills

- Databricks Data EngineeringTools
- Databricks GenAI implementation

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Manager - Data Engineering (Bengaluru)
🏢 Latentview
📍 Bengaluru