Job Title: Azure Data Architect
Job Type: Full-Time
Work Mode: Hybrid
Location: Bengaluru, Hyderabad, Chennai
Experience: 12 – 18 Years
Role Overview
The Data Architect is responsible for designing and governing enterprise-scale data architectures that enable secure, scalable, and high-performance data solutions with Databricks on cloud platform. This role focuses on building modern data platforms, driving best practices, and ensuring alignment with business objectives across analytics, engineering, and governance teams.
Key Responsibilities
Architecture & Strategy
- Define the enterprise data architecture vision and standards for Lakehouse and cloud-native platforms.
- Develop reference patterns for ingestion, transformation, modeling, and governance using Databricks
- Drive adoption of modern frameworks such as DBT for transformation and Airflow for orchestration.
- Enable privacy-preserving analytics through Clean Room solutions and architect reusable data products.
- Ensure designs support scalability, cost optimization, and future-proofing for advanced analytics and AI.
- Drive adoption of Medallion architecture and advanced Lakehouse capabilities (Photon, Delta Live Tables, AI/BI).
- Drive API-based integrations for data services and interoperability.
- Evaluate Databricks features, cost models, runtimes, governance controls, and roadmap impact.
Technical Design Leadership
- Provide architectural guidance to engineering teams for building pipelines and APIs.
- Establish reusable frameworks for ETL/ELT, streaming, and batch processing.
- Integrate Power BI for analytics and reporting, ensuring seamless connectivity with Lakehouse architecture.
- Implement telemetry-driven observability using Splunk/Datadog and enforce DevOps best practices for CI/CD.
- Guide teams on modular designs leveraging Microservices, containerization (Docker/Kubernetes), and infrastructure automation (Terraform) as good-to-have capabilities.
- Review design specifications, data models, pipeline patterns, and solution architectures across squads.
- Ensure cloud-native designs aligned with AWS platform best practices.
Governance, Security & Optimization
- Define standards for data governance, lineage, metadata, and cataloging (e.g., Atlan, Unity Catalog).
- Implement security policies, IAM controls, and compliance frameworks across cloud environments.
- Optimize performance and cost through cluster strategy, workload tuning, and automation.
- Establish observability and telemetry frameworks for operational health and proactive issue resolution.
Cross-Functional Collaboration
- Partner with enterprise architects, product owners, and business stakeholders to validate architectural fit for new and existing use cases.
- Support migration strategies from legacy platforms to modern architectures.
- Collaborate with analytics and BI teams to ensure alignment between data architecture and business objectives.
- Support pre-sales, estimations, and sizing for new programs and migrations
AI/LLM Enablement (Advantage)
- Design frameworks for AI-driven automation, metadata enrichment, and data quality validation.
- Enable integration with Agentic AI-based tools for code generation and operational efficiency.
- Prepare architecture for advanced AI capabilities such as model serving and vector search within Lakehouse environments.
Required Skills & Experience
- 12–18+ years in data engineering/architecture with 4–6+ years in Databricks.
- Clean Room solutions, Data Products architecture, Telemetry data frameworks, API design and integration
- Strong understanding of data governance, lineage, metadata, and cataloging platforms.
- Hands-on experience designing enterprise-scale Lakehouse platforms.
- Experience guiding teams of Tech Leads and Engineers.
Deep expertise in:
- Python, PySpark, Spark SQL, Databricks (Lakehouse architecture, Delta Lake, Unity Catalog), SQL (advanced query optimization and modeling)
- AWS / Azure (data services, IAM, networking), Airflow (workflow orchestration)
- Streaming, batch, and real-time architectures
- Splunk/Datadog (observability and telemetry), DevOps (CI/CD, automation)
- Power BI (analytics and visualization), DBT (data transformation)
Good to have:
- Java
- Atlan (data cataloging and governance)
- Microservices architecture
- Docker/Kubernetes (containerization and orchestration)
- Terraform (infrastructure as code)
Preferred Qualifications (Optional)
- Databricks Certified Data Engineer Professional or Databricks Architect certification.
- AWS / Azure certifications.
- Knowledge of LLMs, agentic frameworks, or GenAI automation (strong advantage).
