Must Have Technical/Functional Skills:
• 14+ years of experience in Enterprise Data Architecture, AWS Data Platforms, Databricks, Data Engineering, and Cloud Modernization.
• Proven experience in Enterprise Solution architecture using AWS, Databricks and related technology components across the Data to Insights end to end value chain.
• Strong hands-on expertise in AWS Data Services, Databricks, Databricks Genie, Unity Catalog, Delta Lake, Python, PySpark, and SQL.
• Proven experience designing and implementing Lakehouse Architectures, enterprise data platforms, and modern analytics solutions on AWS.
• Strong understanding of the Databricks Data Intelligence Platform, including Databricks SQL, Workflows, Lakeflow, MLflow, Unity Catalog, Serverless Compute, and Performance Optimization.
• Experience building AI-enabled analytics solutions using Databricks Genie, semantic models, natural language analytics, and governed data access frameworks.
• Expertise in Data Engineering including ETL/ELT, batch and streaming integration, APIs, orchestration, data quality, metadata management, lineage, observability, and DataOps practices.
• Strong knowledge of Enterprise Data Architecture, including data models, data products, lakehouse and warehouse patterns, semantic layers, and master/reference data management.
• Experience implementing Security, Governance, and Compliance controls including IAM/RBAC, encryption, privacy, masking, auditability, retention, and responsible AI practices.
• Proven experience leading architecture definition, HLD/LLD preparation, NFR assessment, migration planning, technical governance, and production readiness activities.
• Experience supporting cloud transformation initiatives, solutioning, RFP responses, effort estimation, and client-facing architecture discussions.
• Good understanding of BFSI regulatory, security, privacy, resilience, and audit requirements.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Management, or a related discipline.
• Preferred certifications in AWS Solutions Architecture, Databricks, Data Engineering, Enterprise Architecture, Security, or AI/ML.
Roles & Responsibilities:
• Engage with business and technology stakeholders to understand data, analytics, AI, and cloud transformation requirements.
• Define end-to-end architecture for AWS Databricks-based data, analytics, and AI platforms.
• Design scalable, secure, and high-performance Lakehouse solutions using AWS Data Services, Databricks, Delta Lake, and Unity Catalog.
• Architect data ingestion, integration, batch, streaming, and dat
a engineering solutions supporting enterprise analytics and AI use cases.
• Design and implement AI-enabled analytics solutions using Databricks Genie, semantic models, and governed data access frameworks.
• Establish data governance, security, metadata, lineage, observability, and responsible AI standards across the platform.
• Lead data platform modernization, migration, and cloud transformation initiatives on AWS.
• Define architecture standards, reusable frameworks, best practices, and DataOps delivery models.
• Provide technical leadership across solution design, implementation, testing, migration, deployment, and production support activities.
• Conduct architecture reviews, identify risks and dependencies, and guide teams on mitigation strategies and best practices.
• Drive platform performance optimization, reliability improvements, and cost optimization across AWS and Databricks environments.
• Collaborate with data engineers, AI specialists, architects, platform teams, and client stakeholders to deliver enterprise-scale solutions.
• Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops.
• Mentor architects and engineers and contribute to capability development, technical assessments, and knowledge-sharing initiatives.
• Develop reusable accelerators, reference architectures, implementation frameworks, and best practices for AWS Databricks and AI solutions.
