Lead Data Engineer Databricks

Lead Data Engineer Databricks

We are seeking a skilled Lead Data Engineer Databricks to contribute to transformative enterprise data platform projects focused on developing data pipelines and logic engines to manage ingest, staging, and multi-tier data product modeling. Additionally, this includes but is not limited to data enrichment using various OEM-specific data warehouse and data lake house platform implementations for consumption via analytics clients. This role requires full life cycle design, build, deployment and optimization data products for multiple large enterprise industry vertical-specific implementations by processing datasets through a defined series of logically conformed layers, models, and views.

Role & Responsibilities:

Collaborate in defining the overall architecture of the solution. This includes knowledge of modern Enterprise Data Warehouse and Data Lakehouse architectures that implement Medallion or Lamda architectures.

Design, develop, test, and deploy processing modules to implement data-driven rules using SQL, Stored Procedures, and Pyspark.

Understands and owns data product engineering deliverables relative to a CI-CD pipeline and standard devops practices and principles.

Build and optimize data pipelines on platforms like Databricks, SQL Server, or Azure Data Fabric.

Hard Skills - Must Have:

Current knowledge of an using modern data tools like (Databricks, FiveTran, Data Fabric and others); Core experience with data architecture, data integrations, data warehousing, and ETL/ELT processes.

Applied experience with developing and deploying custom whl and or in session notebook scripts for custom execution across parallel executor and worker nodes.

Applied experience in SQL, Stored Procedures, and Pyspark based on area of data platform specialization.

Strong knowledge of cloud and hybrid relational database systems, such as MS SQL Server, PostgresSQL, Oracle, Azure SQL, AWS RDS, Aurora or a comparable engine.

Strong experience with batch and streaming data processing techniques and file compactization strategies.

Hard Skills - Nice to Have/It's a Plus:

Automation experience with CICD pipelines to support deployment and integration workflows including trunk-based development using automation services such as Azure DevOps, Jenkins, Octopus.

Advanced proficiency in Pyspark for advanced data processing tasks.

Advance proficiency in spark workflow optimization and orchestration using tools such as Asset Bundles or DAG (Directed Acyclic Graph) orchestration.

Soft Skills / Business Specific Skills:

Ability to identify, troubleshoot, and resolve complex data issues effectively.

Strong teamwork, communication skills and intellectual curiosity to work collaboratively and effectively with cross-functional teams.

Commitment to delivering high-quality, accurate, and reliable data products solutions.

Willingness to embrace new tools, technologies, and methodologies.

Innovative thinker with a proactive approach to overcoming challenges.