Azure Data Engineer

Job Title: Azure Data Engineer

Location : Los Angeles CA (Hybrid)

We are seeking highly skilled Azure Data Engineer with strong expertise in SQL, Python, Datawarehouse, Cloud ETL tools to join our data team. The ideal candidate will design, implement and optimize large-scale data pipeline, ensuring scalability, reliability, and performance. This role involves working closely with multiple teams and business stakeholders to deliver cutting-edge data solutions.

Technical Skills:

• Strong expertise in Databricks (Delta Lake, Unity Catalog, Lakehouse Architecture, Table Triggers, Delta Live Pipelines, Databricks Runtime etc.)
• Proficiency in Azure Cloud Services.
• Solid Understanding of Spark and PySpark for big data processing.
• Experience in relational databases.
• Knowledge on Databricks Asset Bundles and GitLab.

Preferred Experience:

• Familiarity with Databricks Runtimes and advanced configurations.
• Knowledge of streaming frameworks like Spark Streaming.
• Experience in developing real-time data solutions.

Certifications:

Azure Data Engineer Associate or Databricks certified Data Engineer Associate certification. (Optional)

Key Responsibilities:

1. Data Pipeline Development:
• Build and maintain scalable ETL/ELT pipelines using Databricks.
• Leverage PySpark/Spark and SQL to transform and process large datasets.
• Integrate data from multiple sources including Azure Blob Storage, ADLS and other relational/non-relational systems.
2. Collaboration & Analysis:
• Work Closely with multiple teams to prepare data for dashboard and BI Tools.
• Collaborate with cross-functional teams to understand business requirements and deliver tailored data solutions.
3. Performance & Optimization:
• Optimize Databricks workloads for cost efficiency and performance.
• Monitor and troubleshoot data pipelines to ensure reliability and accuracy.
4. Governance & Security:
• Implement and manage data security, access controls and governance standards using Unity Catalog.
• Ensure compliance with organizational and regulatory data policies.
5. Deployment:
• Leverage Databricks Asset Bundles for seamless deployment of Databricks jobs, notebooks and configurations across environments.
• Manage version control for Databricks artifacts and collaborate with team to maintain development best practices.