Job Description

• 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.

• Experience with some Databricks infrastructure.

• Experience with building BI dashboards and visualization.

• Experience with coding agents and best practices (spec-driven development, etc.).

Must Have / Nice to Have Skills Required:

• 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