Build and maintain scalable data pipelines with Databricks, Spark, and PySpark.
Manage data governance, security, and credentials using Unity Catalog and Secret Scopes.
Develop and deploy ML models with MLflow; work with LLMs and embedding-based vector search.
Apply ML/DL techniques (classification, regression, clustering, transformers) and evaluate using industry metrics.
Design data models and warehouses leveraging dbt, Delta Lake, and Medallion architecture.
Work with healthcare data standards and medical terminology mapping.

Hands-on experience with the Databricks platform, including:
Unity Catalog: Managing data governance, access control, and auditing across workspaces.
Secret Scopes: Secure handling of credentials and sensitive configurations.
Apache Spark / PySpark: Writing performant, scalable distributed data pipelines.
MLflow: Managing ML lifecycle including experiment tracking, model registry, and deployment.
Vector Search: Working with vector databases or search APIs to build embedding-based retrieval systems.
LLMs (Large Language Models): Familiarity with using or fine-tuning LLMs in Databricks or similar environments.
Experience designing and maintaining robust data pipelines:
Data Modeling & Warehousing: Dimensional modeling, star/snowflake schemas, SCD (Slowly Changing Dimensions).
Familiarity with dbt, Delta Lake, and the Medallion architecture (Bronze, Silver, Gold layers)

Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation.
Be part of a global team with equal opportunities for collaboration across continents and cultures.
Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards.

Data Engineer, Databricks

Key skills