Azure Data Architect

Position: Data Architect

Experience: 15+ years

Key Responsibilities

• Lead and grow the Data & Analytics practice, including strategy, delivery, capability development and team growth.

• Provide technical and architectural leadership across Azure Data and Databricks platforms.

• Design and oversee modern data architectures including Lakehouse, Data Lake, Data Warehouse and Data Engineering solutions.

• Lead large-scale data transformation and modernization programs for enterprise clients.

• Drive presales, solutioning, RFP/RFI responses, proposals and client presentations.

• Build strong relationships with CXO-level stakeholders and technology leadership.

• Manage and mentor data architects, engineers, analysts and technical leads.

• Define technology standards, reusable frameworks, accelerators and best practices.

• Partner with sales and business teams to identify new opportunities and expand existing accounts.

• Own delivery governance, project quality, utilization, margins and overall practice performance.

• Stay current with emerging technologies across Data Engineering, Databricks, Azure, AI/ML and Generative AI.

Required Skills

• 15+ years of experience in Data Engineering / Data Architecture / Data & Analytics.

• Strong hands-on/architectural experience with Microsoft Azure.

• Strong expertise in Databricks, including Lakehouse architecture, Delta Lake, Unity Catalog and data engineering.

• Experience with Azure Data Factory, ADLS, Azure Synapse, Azure Functions and related Azure data services.

• Strong understanding of PySpark, SQL and ETL/ELT frameworks.

• Experience leading large data engineering / analytics teams and enterprise programs.

• Strong client-facing and stakeholder management skills.

• Proven experience in presales, solutioning and practice development.

• Experience working with global / enterprise customers is preferred.

Good to Have

• Exposure to Generative AI / Machine Learning / AI-led Data Platforms.

• Databricks certifications or Azure certifications.

• Experience in building and scaling a Data & AI practice.

• Strong commercial understanding of revenue, margins, utilization and account growth.