Microsoft Lead Data Architect at DBAce Technologies

Role Purpose

The Microsoft Lead Data Architect owns the end-to-end data architecture for the transformation programme. The role converts business, technical, security, and operational requirements into a scalable Microsoft Azure data-platform design, supported by an actionable HLD, LLD guidance, and migration blueprint.

Experience Profile

• 12–18 years of overall data and technology experience.

• Deep architecture expertise in Microsoft Azure, Azure Databricks, Microsoft Fabric, and modern data-lake or lakehouse platforms.

• Strong experience designing enterprise-scale ingestion, storage, transformation, analytics, governance, and ML/AI capabilities.

• Demonstrated ability to facilitate complex discovery and architecture workshops.

• Proven experience creating HLDs, reviewing LLDs, and designing large-scale data-migration strategies.

Key Responsibilities

• Plan, design, and facilitate discovery and data-architecture workshops with business and technical stakeholders.

• Lead an end-end programme covering:

  • Data ingestion

  • Data storage

  • Data transformation

  • Security and access management

  • Azure platform foundations

  • Analytics and reporting

  • Machine learning and artificial intelligence

• Capture functional and non-functional requirements, constraints, dependencies, and design decisions.

• Produce the HLD for the target Microsoft data platform.

• Define the architecture standards and patterns that guide the development of LLDs.

• Review LLDs to confirm alignment with approved HLD decisions and resolve gaps or deviations.

• Design the target architecture across ingestion, storage, processing, orchestration, serving, governance, security, analytics, and ML/AI layers.

• Define appropriate usage patterns for Azure Databricks, Microsoft Fabric, Azure Data Lake, and related Microsoft services.

• Develop the migration blueprint, including migration patterns, sequencing, dependencies, reconciliation, cutover, and risk management.

• Work closely with data engineering, Purview, Power BI, security, infrastructure, analytics, and ML/AI teams.

• Present design recommendations to programme stakeholders and client architecture review boards.

• Support delivery teams during implementation by clarifying decisions and resolving architecture issues.

Key Deliverables

• Discovery and architecture workshop programme

• Workshop materials and documented outcomes

• Microsoft data-platform HLD

• LLD standards and review findings

• Target-state Azure data architecture

• Databricks, Fabric, and data-lake architecture patterns

• Ingestion, transformation, storage, security, and serving patterns

• Analytics and ML/AI enablement architecture

• Data migration blueprint

• Architecture decisions, assumptions, and dependencies