Cloud Data AI Architect

The role will partner with business and engineering teams to define architecture, guide implementation, and ensure security, performance, and cost-efficiency across the data and AI landscape.

• Key Responsibilities
• 1) Solution Architecture (Snowflake AWS Analytics)
• Own end-to-end architecture for cloud data platforms leveraging Snowflake and AWS-native analytics services (e.g., S3, Glue, Lake Formation, Athena, Redshift, EMR/Kinesis/MSK as applicable).
• Define target-state patterns for historical incremental loads, batch/real-time ingestion, and scalable transformations using tools like dbt and orchestration frameworks (e.g., Airflow).
• Design Lakehouse / Medallion style architectures and integration approaches between AWS data lake and Snowflake analytics marts, including modern table formats (e.g., Iceberg where applicable).
• Lead architecture reviews, trade-off decisions (performance/cost/security), and ensure solutions meet non-functional requirements.
• 2) Data Engineering Enablement & Integration
• Guide teams on building robust ELT/ETL pipelines, metadata management, and data quality controls (audit, reconciliation, balance/control frameworks).
• Establish best practices for Snowflake performance tuning, query optimization, and workload management.
• Define ingestion patterns from enterprise sources (e.g., SAP and other systems) into AWS/Snowflake using standard integration approaches
• 3) Analytics & BI (Power BI)
• Architect and govern Power BI semantic models/datasets, ensuring consistent metrics (“single version of truth”), strong performance, and reusability.
• Implement enterprise-grade security for BI (e.g., Row-Level Security (RLS), access controls, dataset governance) aligned with data platform security design.
• Partner with BI developers and stakeholders to deliver scalable reporting patterns and lifecycle governance (certification, promotion, workspace standards).
• 4) GenAI Architecture (Amazon Bedrock)
• Design GenAI solution patterns using Amazon Bedrock, including Agents, Knowledge Bases (RAG), and safe deployment controls using Guardrails.
• Define agent tool/action patterns (action groups), retrieval grounding strategy, and observability/tracing for production readiness.
• 5) Security, Governance & Compliance
• Implement fine-grained access control across the lake/warehouse ecosystem and enforce governance controls for data access and auditability.
• Define governance integration approaches beyond native capabilities when needed (catalog/lineage tools, stewardship workflows).
• 6) Stakeholder Leadership & Delivery
• Lead technical discovery, translate business needs into architecture, and produce design artifacts (HLD/LLD, roadmaps, standards).
• Mentor engineers, enforce engineering standards, and collaborate across product, platform, security, and operations teams.

• Required Qualifications (Must Have)
• Experience
• 10 years in data platform / analytics architecture (or equivalent depth in engineering architecture).
• Proven architecture experience with Snowflake (warehouse design, data modeling, performance tuning, governance/security).
• Strong hands-on AWS analytics/data services experience, especially integrating lake warehouse and enabling governed access.