Expert Data Engineer (Databricks)

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