DataBricks Data Engineer

Role: Databricks Data Engineer

Location: Remote

Duration: 12+ Months

Must be Databricks certified.

 

Job Summary

The Databricks Data Engineer owns the end-to-end migration strategy, target architecture design, and technical execution of moving legacy ETL workloads to the Databricks Lakehouse. He will establish migration standards, optimize PySpark pipelines, orchestrate complex data workflows, and deploy proprietary automation tools to ensure a seamless, high-performing transition from legacy systems.

 

Key Responsibilities

Architectural Strategy & Governance

•          Design the target Databricks Lakehouse architecture utilizing Delta Lake, Photon, and Unity Catalog.

•          Establish global code refactoring standards, optimization benchmarks, and PySpark best practices.

•          Resolve highly complex dependency mappings and architect seamless, zero-downtime dual-run strategies.

•          Lead the technical deployment and integration of specialized migration accelerators

Hands-on Engineering & Optimization

•          Review automated output from migration tools and manually refactor complex legacy logic into high-performing PySpark notebooks.

•          Eliminate legacy anti-patterns such as massive row-by-row processing and inefficient lookups.

•          Optimize PySpark code performance using advanced Spark features including Z-Ordering, partitioning, and caching.

•          Build robust Databricks Workflows and orchestrate complex DAGs based on comprehensive source lineage.

 

Technical Skills & Competencies

•          Core Platforms: Databricks, Delta Lake, Unity Catalog, Photon, DataStage.

•          Languages & Frameworks: PySpark, Python, SQL, Shell Scripting.

•          Cloud & DevOps: AWS alongside CI/CD deployment pipelines.

•          Orchestration: Apache Airflow, Databricks Workflows.