DE&A - Sr. Data Engineer - Databricks

We are looking for a hands-on Senior Data Engineer with 7 to 9 years of experience to design, build, and optimize large-scale data pipelines and lakehouse solutions on Databricks. The ideal candidate is a strong individual contributor who stays current with the latest Databricks platform capabilities — including Unity Catalog, Lakeflow (Delta Live Tables), Lakebase, and Databricks' expanding AI/agent tooling — and can apply them to solve real-world data engineering problems at scale.

Key Responsibilities:

- Design, develop, and maintain scalable ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake for batch and streaming workloads.

- Build and manage declarative pipelines using Lakeflow / Delta Live Tables (DLT), including expectations, data quality checks, and change data capture (CDC).

- Implement and manage data governance, access control, lineage, and data sharing using Unity Catalog across multiple workspaces and clouds.

- Optimize Spark jobs and Databricks clusters for performance and cost, leveraging Photon, serverless compute, auto-scaling, and job clustering best practices.

- Design and implement medallion architecture (bronze/silver/gold) data models and lakehouse patterns for analytics and ML consumption.

- Work with Databricks Workflows (Jobs) to orchestrate multi-task pipelines, including dependency management, retries, and monitoring/alerting.

- Integrate Databricks with cloud-native services (AWS/Azure/GCP) such as S3/ADLS/GCS, Kafka/Event Hubs/Kinesis, Glue/ADF, and IAM/Entra ID for secure, automated data flows.

- Apply CI/CD practices for Databricks using Databricks Asset Bundles (DABs), Repos, and Git integration; automate deployments across dev/test/prod.

- Evaluate and adopt newer Databricks capabilities — Lakebase (serverless Postgres on the lakehouse), Unity Catalog Metrics, Genie/Agent Bricks, Mosaic AI, and real-time/streaming enhancements — and recommend where they add value to existing pipelines.

- Implement data quality, testing, and observability frameworks (e.g., Great Expectations, DLT expectations, Lakehouse Monitoring) to ensure trustworthy, production-grade data.

- Collaborate with data scientists, analysts, and business stakeholders to understand requirements and translate them into robust, reusable data engineering solutions.