Azure DBX

Fractal Analytics•California, CA

5d•$120,000 - $140,000•Onsite

About The Position

We are seeking a highly skilled Senior Azure Databricks Engineer to design, develop, and optimize enterprise-scale data platforms on Microsoft Azure. The ideal candidate will have strong hands-on experience with Azure Databricks, Data Engineering, Cloud Architecture, and Data Integration solutions. This role requires close collaboration with business stakeholders, architects, data scientists, and cross-functional teams to deliver scalable and high-performance data solutions.

Requirements

Azure Databricks (DBX)
Azure Data Factory (ADF)
Azure Data Lake Storage Gen2 (ADLS)
Azure Synapse Analytics
Azure Key Vault
Azure DevOps
Azure Monitor and Log Analytics
PySpark
Spark SQL
Python
SQL
Delta Lake
Data Modeling
ETL/ELT Development
Data Warehousing Concepts
Git
CI/CD Pipelines
Release Management
SQL Server
Azure SQL Database
PostgreSQL

Nice To Haves

Experience implementing Lakehouse architectures.
Experience with Streaming solutions using Event Hub or Kafka.
Knowledge of Machine Learning workflows in Databricks.
Exposure to Healthcare, Life Sciences, Pharmaceutical, or Financial Services domains.
Experience working directly with client stakeholders in onsite environments.
Strong understanding of data security, governance, and data quality frameworks.
Snowflake (Preferred)
Infrastructure as Code (Terraform preferred)

Responsibilities

Design, develop, and maintain scalable data pipelines using Azure Databricks.
Build and optimize ETL/ELT workflows for processing large-volume structured and unstructured datasets.
Develop data solutions using Spark (PySpark/Scala) within Azure Databricks.
Implement Medallion Architecture (Bronze, Silver, Gold layers) for modern data platforms.
Integrate data from multiple sources including APIs, databases, files, and streaming platforms.
Collaborate with solution architects and business teams to understand data requirements.
Design and implement data models supporting analytics, reporting, and AI/ML initiatives.
Optimize Spark jobs and cluster configurations for performance and cost efficiency.
Develop CI/CD pipelines for Databricks deployments using Azure DevOps.
Implement data governance, security, monitoring, and compliance best practices.
Troubleshoot production issues and support business-critical data operations.
Mentor junior engineers and conduct technical reviews.