Azure Data Lead

Summary role description:

Hiring for an Azure Data Lead role with a fast-growing technology services provider delivering innovative IT consulting, cloud, and digital solutions.

Company description:

Our client is a global organization with a long history and presence across multiple regions. The company works with businesses in different industries to support their technology and digital needs. It provides a range of modern solutions and services that help improve operations, enhance experiences, and enable future growth. Known for its expertise and collaborative approach, the organization continues to be a trusted partner for companies undergoing various stages of digital change.

Role details:

• • • • Title / Designation: Azure Data Lead
      • Location: Guindy, Chennai
      • Work mode: On-site

Role & responsibilities:

• Lead the design, build, and operation of the Azure Databricks data platform, ensuring scalability, reliability, and ease of use.
• Implement and manage the lakehouse and Medallion architecture (Bronze, Silver, Gold) to organize data for analytics, reporting, and AI.
• Develop and maintain end-to-end data pipelines ingesting data from APIs, files, databases, and other internal and external sources.
• Ensure strong data governance, security, and compliance using Unity Catalog, including access control, lineage, and metadata management.
• Manage, mentor, and provide technical leadership to data engineers, enforcing best practices and supporting career development.
• Design and manage Delta Lake tables and Azure Data Lake storage, enabling reliable processing, incremental loads, and cost-effective storage.
• Optimize Databricks clusters, Spark jobs, and pipelines for performance, scalability, monitoring, and cost efficiency.
• Establish data quality, error handling, and observability practices, ensuring stable and trustworthy data pipelines.
• Build secure and resilient data solutions using Azure services such as Synapse Analytics, Key Vault, and Azure Monitor.
• Own CI/CD, automation, and deployment standards using Git and Azure DevOps.
• Collaborate with business, analytics, data science, and AI teams to deliver reusable data models and support ML, GenAI, and LLM workloads.
• Define an AI-ready data roadmap and advise stakeholders on data modernization, transformation, and future analytics strategy.
• Continuously improve the platform by tracking industry trends and recommending architectural and tooling enhancements.

Candidate requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 8+ years of experience in data engineering, including at least 2 years in a technical lead or people management role.
• Strong hands-on expertise in Azure data engineering, including Databricks, PySpark, SQL, and core Azure data services.
• Proven experience designing and leading end-to-end data pipelines and lakehouse solutions, using Medallion architecture, Unity Catalog, Delta Lake, and Iceberg on Databricks.
• Solid understanding of big data concepts, lakehouse architectures, and cloud-based ETL orchestration.
• Hands-on experience with Azure services such as ADLS, Data Factory, Synapse Analytics, Key Vault, SQL Server, and Azure Monitor.
• Experience building CI/CD pipelines and automation using Git and Azure DevOps, with strong focus on code quality and standards.
• Demonstrated leadership skills, including mentoring engineers, scaling teams, and effective communication with technical and non-technical stakeholders.
• Strong analytical and strategic thinking abilities, with experience managing complex problems and stakeholder expectations.
• Experience collaborating with data science, analytics, and business teams to support ML and AI use cases, including data preparation and optimization.
• Ability to advise and consult on data strategy and architecture for large-scale digital transformation initiatives.
• Relevant Azure or Databricks certifications are preferred.

Selection Process:

• Two technical interviews
• HR round

Recruiter Details:

• [email protected]