Azure Data Engineer

Work Type:

Hybrid (2–3 days onsite per week, or specify as needed)

Years of Experience Required:

5–10 years

Job Description

We are seeking an experienced L4 Engineer with strong hands‑on expertise in modern data engineering, cloud platforms, and application development. The successful candidate will work across Azure data services, Databricks workflows, and Java/Python‑based engineering to support complex data and analytics solutions. This role requires excellent problem‑solving skills, strong coding abilities, and the ability to work in a fast‑paced, collaborative environment.

Key Responsibilities

• Design, develop, and optimize data pipelines using Azure Databricks, Azure Data Factory, and Azure SQL.
• Build scalable data engineering and analytics solutions following best practices in cloud architecture.
• Develop microservices, APIs, and backend components using Java Spring Boot and Python.
• Utilize GitHub and GitHub Copilot for version control, code quality, and accelerated development.
• Implement MLOps workflows and model lifecycle automation using H2O, Databricks ML, or similar technologies.
• Work with TigerGraph for graph‑based data modeling, queries, and analytics optimization.
• Troubleshoot critical production issues and perform root‑cause analysis across cloud workloads.
• Collaborate with cross‑functional teams including data engineers, architects, and data scientists.
• Ensure adherence to security, scalability, and performance best practices across the full development lifecycle.

Top Required Skills

• Azure Databricks (Primary Skill) – Strong experience building notebooks, pipelines, Delta Lake solutions, and distributed data processing workloads.
• Expertise in building ETL/ELT workflows, mapping data flows, and integrating data sources.
• Solid programming knowledge for backend and data engineering tasks using Java Spring Boot and Python.

Additional Technical Skills (Preferred)

• TigerGraph or other graph database technologies.
• Experience with cloud security, CI/CD, and DevOps best practices.