Roles and Responsibilities :

- 9–10 years of hands-on experience designing, building, and optimizing large-scale (TB/PB) data engineering solutions and distributed data pipelines.

- Strong, hands-on expertise with Databricks — Spark/PySpark, Delta Lake, lakehouse architecture, Unity Catalog, Databricks Workflows, and performance/cost optimization.

- Robust hands-on experience with Snowflake — data modeling, virtual warehouses, Snowpipe, Streams & Tasks, performance tuning, RBAC/security, and cost governance.

- Proven Generative AI / LLM data engineering experience — building ingestion and retrieval pipelines for RAG, chunking, and embedding generation, vector stores, and feature/context pipelines that feed LLM applications.

- Hands-on with at least one major cloud platform (AWS, Azure, or GCP)



and comfortable working across cloud data and AI/ML services; strong understanding of networking, IAM/security, and cost optimization.

- Expert-level SQL and strong Python for data engineering;

experience building automated, reusable ETL/ELT frameworks (batch and streaming).

- Experience with data orchestration (Airflow / Databricks Workflows / equivalent) and CI/CD for data (Git-based pipelines, Terraform or equivalent IaC); exposure to Docker/Kubernetes is a plus.

- Solid grounding in data governance, data quality, lineage, cataloging, and security across the data lifecycle.

- Experience mentoring and technically leading a team of engineers, driving design reviews, code quality, and delivery standards.

📌 Lead AI Engineer (Bengaluru)
🏢 Impetus Technologies
📍 Bengaluru