Azure Data Engineer

Tiger Analytics; Diamond Bar (CA); On-site; USD 90,000 - 130,000; Full time

Job summary

Tiger Analytics in Diamond Bar, CA is seeking an Azure Data Engineer for a full-time, local or nearby engagement. You will design, build and maintain scalable ETL/ELT pipelines using Azure Data Factory, Azure Synapse, and Azure Databricks, and you will help model data for reporting.

You will implement data governance, secure storage, and automated deployments, collaborating with BI teams and ensuring cost-efficient cloud operations.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines with Azure Data Factory, Synapse, and Databricks.
• Architect and manage secure enterprise storage (ADLS Gen2, Delta Lake, and relational/NoSQL databases).
• Ingest and process high-volume batch data and real-time streaming workloads.
• Optimize PySpark jobs and data pipeline performance to minimize execution time and cloud costs.
• Collaborate with BI and analytics teams to model dimensional data marts (Star/Snowflake schemas) for reporting.
• Implement data governance, automated data quality validation, and end-to-end lineage tracking (e.g., Purview).
• Secure data pipelines and storage using Azure RBAC, Key Vaults, and encryption standards.
• Automate infrastructure deployments and code releases using Azure DevOps, GitHub Actions, and CI/CD best practices.

Job description

Azure Data Engineer (Full-time / Local or Nearby resources required)

Candidate Preference: This team operates on a onsite schedule out of our Diamond Bar, CA facility. To ensure seamless in-person collaboration and team alignment, we are seeking local or nearby Greater Los Angeles/Inland Empire-based candidates.

Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Azure Synapse, and Azure Databricks.
• Architect and manage secure enterprise cloud storage, including ADLS Gen2, Delta Lake, and relational/NoSQL databases.
• Ingest and process both high-volume batch data and real-time streaming workloads.
• Optimize database queries, PySpark jobs, and data pipeline performance to minimize execution time and cloud costs.
• Collaborate with BI and analytics teams to model dimensional data marts (Star/Snowflake schemas) for reporting.
• Implement data governance, automated data quality validation, and end-to-end lineage tracking (e.g., via Microsoft Purview).
• Secure data pipelines and storage using Azure Role-Based Access Control (RBAC), Key Vaults, and encryption standards.
• Automate infrastructure deployments and code releases using Azure DevOps, GitHub Actions, and CI/CD best practices.