Databricks Developer

Design, build, and manage data products and event driven architecture using Databricks Lakehouse principles.
Design end-to-end data pipelines using PySpark, Databricks SQL.
Define Data Product blueprint including domain boundaries, ownership, SLAs, documentation, quality rules.
Implement modern ingestion frameworks using Auto Loader, Delta Live Tables, Workflows.
Develop multi-zone medallion architecture (Bronze/Silver/Gold) using Delta Lake.
Lead Databricks on AWS integration including S3 access, IAM roles, VPC networking.
Implement Unity Catalog governance frameworks, fine-grained permissions, lineage tracking.
Drive automation & DevOps practices using Git, Repos, Databricks CLI/SDK, CI/CD pipelines.
Build and optimize Spark workloads for performance and cost control (Photon/Serverless tuning).
Lead performance reviews, code quality checks, and data engineering guidance for teams.
Operationalize Machine Learning and advanced analytics with MLflow/Feature Store/Model Serving.
Monitor and enhance reliability using job monitoring, observability dashboards, alerts.
Hands-on development of Power BI dashboards, DAX measures, data models, DirectQuery/Import mode
Evaluate new Databricks features and adopt innovation into technical roadmap.
Technical Skills Required
Strong programming experience in PySpark, SQL & Python for production-grade data pipelines.
Deep mastery in Databricks, Delta Lake, Unity Catalog, Workflows, SQL Warehouses.
Expert knowledge of AWS services: S3, Glue, Lambda, EMR, Step Functions, CloudWatch, VPC networking.
Experience building data products with versioning, discoverability, contracts, metadata & lineage.
Good understanding of Infra-as-Code: Terraform (workspace, clusters, UC policies, jobs, service principals).
Strong foundation in data modeling, schema design, Lakehouse & Data Mesh principles.
Familiar with governance & security frameworks (encryption, tokenization, row/column controls).
Experience with enterprise integrations (Power BI).