What are the responsibilities and job description for the Azure Databricks Data Architect position at Ascendum System Private Limited?
Responsibilities
- Architect scalable, high-performing data solutions to meet both functional and non-functional requirements.
- Own the architecture, design, and optimization of Databricks platforms within cloud-based ecosystems (Azure preferred).
- Serve as the technical lead and trusted advisor for clients, facilitating architecture workshops, requirements discovery sessions, and whiteboard discussions to design scalable Databricks Lakehouse solutions that align with business objectives, data strategy, and modernization initiatives.
- Partner directly with business and technical stakeholders to define end-to-end data architectures, presenting and defending solution designs while providing hands-on guidance for Databricks, Spark, Delta Lake, cloud platforms (Azure/AWS/GCP), data governance, and CI/CD best practices throughout the project lifecycle.
- Lead hands-on implementation of data pipelines and infrastructure using best practices for DevOps and infrastructure-as-code.
- Partner with data engineering teams to ensure platform performance, reliability, and maintainability.
- Integrate Databricks solutions across systems, aligning with enterprise architecture standards and delivery best practices.
- Create and review architecture and solution design documents for large-scale data initiatives.
- Evangelize reuse and modular design through shared services and common data models.
- Enforce architectural standards and guide teams on patterns, tools, and delivery methods.
- Mentor and provide technical guidance to engineers during development and delivery.
- Contribute to risk management through proactive identification and mitigation of technical delivery risks.
- Operate at varying levels of abstraction—solutioning high-level architecture while diving deep when needed.
- Bachelor’s or master’s degree in computer science, Information Technology, or related field.
- 8 years of experience in data architecture, cloud data warehousing, or large-scale data integration.
- 3 years of hands-on experience delivering production-ready Databricks solutions.
- Strong proficiency in Python, SQL, data modeling, and Azure services (Data Factory, Event Hub, Synapse, DevOps).
- Experience implementing infrastructure using Terraform, ARM templates, or similar IAC tools.
- Comfort working in Agile/Scrum environments and supporting CI/CD pipelines.
- Excellent communication skills—you can explain complex ideas to tech teams and business stakeholders alike.
- Bonus: Familiarity with ML lifecycle concepts and MLOps tools, even if you're not a model builder yourself.