As a Senior Azure Databricks Developer, you will:
- Design, develop, and maintain scalable data processing solutions using Azure Databricks.
- Build and optimize batch and streaming data pipelines using Python, PySpark, and SQL.
- Design and implement robust data models within the Databricks ecosystem.
- Manage multiple parallel data processing workloads on shared datasets while ensuring performance and reliability.
- Collaborate with cross-functional teams to deliver high-quality data solutions aligned with business requirements.
- Implement CI/CD pipelines using Azure DevOps and YAML-based deployment frameworks.
- Utilize Infrastructure as Code (ARM/Bicep) to automate cloud resource provisioning and management.
- Participate in Agile ceremonies and contribute to continuous improvement initiatives.
- Ensure solution quality, maintainability, scalability, and adherence to engineering best practices.
- Make informed technical decisions within a complex and evolving cloud data architecture.
What You Bring to the Table:
- 6–8 years of overall experience in software engineering, data engineering, or cloud data platform development.
- Strong hands‑on experience with Azure Databricks as a primary development platform.
- Deep expertise in Python, PySpark, and SQL for large-scale data processing.
- Proven experience building and managing both streaming and batch processing solutions.
- Solid experience with Azure cloud services and CI/CD implementation using YAML pipelines.
- Hands‑on experience with Infrastructure as Code tools, particularly ARM Templates and/or Bicep.
- Strong understanding of distributed data processing concepts and performance optimization.
- Experience working in Agile development environments.
- Exposure to end‑to‑end data platform architectures is an added advantage.
You Should Possess the Ability to:
- Design reliable and scalable data processing frameworks within Azure Databricks.
- Handle complex data transformation and processing requirements efficiently.
- Optimize data workloads for performance, scalability, and cost‑effectiveness.
- Manage concurrent processing pipelines and shared data dependencies.
- Identify, troubleshoot, and resolve technical issues proactively.
- Communicate technical concepts effectively to stakeholders and team members.
- Balance engineering best practices with practical business requirements.
- Drive continuous improvement initiatives and reduce technical debt.
- Deliver maintainable, production‑grade solutions with a strong focus on quality.
What We Bring to the Table:
- Opportunity to work on large‑scale cloud‑native data platforms and modern data engineering initiatives.
- Exposure to cutting‑edge Azure and Databricks technologies.
- Collaborative Agile work environment focused on innovation and continuous learning.
- Challenging technical projects that encourage ownership and professional growth.
- A culture that values quality engineering, pragmatic problem‑solving, and teamwork.
- Opportunities to contribute to architecture decisions and influence technical direction.
- Dynamic and supportive team environment focused on delivering impactful solutions.
Let’s Connect
Want to discuss this opportunity in more detail? Feel free to reach out.