Databricks DE with Azure

Fractal Analytics•California, CA

1d•$120,000 - $140,000

About The Position

We are seeking an experienced Azure Databricks Data Engineer to design, develop, and maintain scalable cloud-based data solutions. The ideal candidate will have strong expertise in Azure Data Engineering technologies, Databricks, data pipelines, data warehousing, and big data processing. This role involves collaborating with business stakeholders, architects, data scientists, and application teams to deliver high-quality data platforms that support analytics and AI initiatives.

Requirements

Azure Services: Azure Databricks, Azure Data Factory (ADF), Azure Data Lake Storage Gen2 (ADLS), Azure Synapse Analytics, Azure Key Vault, Azure Monitor, Azure DevOps
Data Engineering: PySpark, Spark SQL, Delta Lake, ETL/ELT Pipeline Development, Data Modeling, Data Warehousing Concepts, Data Governance and Data Quality
Programming: Python, SQL
DevOps & Automation: Azure DevOps, Git, CI/CD Pipelines
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
6-10 years of experience in Data Engineering and Analytics.
Minimum 3+ years of hands-on experience with Azure Databricks.
Strong experience with PySpark, SQL, and cloud-native data platforms.
Experience building enterprise-scale data lakes and data warehouses.
Knowledge of performance tuning and optimization techniques in Spark.
Experience working in Agile delivery environments.

Nice To Haves

Scala
Infrastructure as Code (Terraform preferred)
Azure Data Engineer Associate Certification (DP-203).
Experience with Unity Catalog and Databricks Workflows.
Knowledge of Delta Live Tables (DLT).
Experience supporting AI/ML workloads and data science teams.
Exposure to Snowflake, Power BI, or Microsoft Fabric.
Healthcare, Life Sciences, or Pharma domain experience is a plus.

Responsibilities

Design, build, and optimize scalable data pipelines using Azure Databricks.
Develop ETL/ELT workflows for large-scale structured and unstructured datasets.
Implement batch and real-time data processing solutions using PySpark and Spark SQL.
Build and maintain data ingestion frameworks from multiple data sources.
Develop and manage data lake solutions using Azure Data Lake Storage (ADLS Gen2).
Integrate data from cloud and on-premises systems into enterprise data platforms.
Implement data quality, validation, and monitoring frameworks.
Optimize Spark jobs for cost, scalability, and performance.
Design dimensional models and support data warehouse initiatives.
Collaborate with business users to understand reporting and analytics requirements.
Automate deployment processes using CI/CD pipelines.
Participate in architecture discussions and provide technical leadership to junior engineers.
Ensure adherence to security, governance, and compliance standards.