- Design, build, and maintain Databricks data pipelines (ETL/ELT) for ingestion, transformation, and orchestration using Spark/Delta Lake/Databricks Workflows.

- Operationalize machine learning models by building inference pipelines that invoke models authored by data scientists (batch or real-time), ensuring consistency between training and inference environments.

- Ensure data reliability, quality, and observability through robust validation, monitoring, alerting, and automated recovery mechanisms.

- Collaborate closely with data scientists to productionize models, manage model deployment lifecycles, and optimize inference performance and cost.

- Implement best-practice DevOps/MLOps processes such as CI/CD for pipelines, model versioning, environment promotion, and infrastructure-as-code.

- Optimize performance and cost across compute clusters, jobs, and storage layers.

- Implement and manage the enterprise data catalog, including schema design, table ownership, lineage, governance, and documentation using Unity Catalog.

Tech Stack & Requirements :

- Databricks platform experience

- Python development for data processing and ETL pipelines

- Unity Catalog knowledge

- AWS data services (S3, IAM, VPC, potentially Glue/Lambda)

- Data lake/lakehouse architecture patterns

- Dashboard building experience

Nice to Have :

- RESTful API design and development (Flask, FastAPI, or similar)

- Authentication/authorization patterns (OAuth, API keys, IAM roles)

- Query optimization and performance tuning

- PySpark optimization experience

- ML/AI pipeline experience

- Databricks AI/BI