Description

Key Responsibilities

Design, develop, and maintain scalable data engineering solutions using Databricks and AWS.

Implement and optimize Delta Lake and Lakehouse architecture.

Develop high-performance ETL/ELT pipelines using PySpark and Scala Spark.

Build and manage high-volume, production-grade data processing workflows.

Implement real-time and streaming data pipelines using Databricks and Delta Live Tables (DLT).

Implement and support MLOps solutions within the Databricks ecosystem.

Apply Data Mesh architecture principles to enterprise data platforms.

Optimize data processing workloads for performance, scalability, and cost efficiency.

Monitor and troubleshoot production data pipelines to ensure reliability and data quality.

Collaborate with data engineering, architecture, and ML teams to deliver enterprise data solutions.

Mandatory Skills

Deep knowledge of Databricks and AWS ecosystems.

Strong experience with Delta Lake and Lakehouse Architecture.

Knowledge of Lakeflow and Lakebase.

Hands-on expertise in PySpark and Scala Spark.

High-volume production-grade Databricks ETL/ELT engineering.

Experience with Streaming and Delta Live Tables (DLT).

MLOps implementation and experience within Databricks.

Knowledge of Data Mesh architecture.

Advanced cost optimization and performance engineering.

Strong experience as a Databricks and AWS Data Engineer

Key Skills

Education

Bachelor's degree