Senior Architect - Engineering

• Responsibilities Experience 16–18 years overall experience 5–8+ years in data & analytics architecture, analytics engineering, or enterprise analytics roles 3+ years hands-on experience designing on modern cloud data platforms (Databricks, Snowflake, or equivalent) Role Summary The Senior Data & Analytics Architect designs, governs, and evolves the enterprise analytics architecture to enable trusted, scalable, and self-service decisioning. This role translates business outcomes into an actionable analytics architecture across modern cloud data platforms, ensuring data is consistent, governed, high-performing, and AI-ready. You will lead warehouse/Lakehouse architecture, data products, semantic layers, and metrics governance—partnering with engineering, BI, and data science to standardize patterns, develop best practices, architecture decisions and accelerate delivery. Success in this role is measured by faster time-to-insight, consistent KPI definitions across the enterprise, improved platform performance/cost, and increased reuse of governed data products.

Key Responsibilities Analytics & Data Architecture:

• Define and maintain the enterprise data and analytics architecture blueprints, vision, principles, and target states.
• Ensure data ecosystem alignment with enterprise architecture strategy, business capabilities, and operating models.
• Collaborate to manage, maintain, and mature the health of data and analytics capabilities for the enterprise
• Define and maintain the enterprise analytics architecture across modern data platforms including: o Analytical data models and marts o Semantic and metrics layers o Cloud data lake and warehouse solutions
• Design analytics solutions optimized for: o BI reporting o Self-service analytics o Advanced analytics and AI consumption Classification: Internal Use
• Ensure analytics architectures scale with growing data volumes and concurrent users
• Own the analytics architecture roadmap (target state, phased migration, and decommission plan) aligned to business priorities
• Define reference architectures, standards, and guardrails; serve as design authority for analytics modeling and consumption patterns Data Modeling & Semantic Layer
• Own analytical and dimensional data models for enterprise reporting
• Design and govern: o Conformed dimensions o Business metrics and KPIs o Semantic layers on top of Databricks / Snowflake
• Enable tool agnostic analytics consumption across multiple BI platforms
• Ensure consistency of definitions across dashboards, reports, and AI use cases Data Products & Marketplace Enablement
• Drive a data-as-a-product approach for analytical datasets, including product ownership, lifecycle management, and adoption
• Define analytics data products hosted on Databricks / Snowflake with clear onboarding, versioning, and support processes, including: o Clear schemas and contracts o Quality thresholds and SLAs o Documented usage patterns
• Enable discoverability, trust, and reuse through catalog and marketplace integration Modern Data Platform Architecture (Snowflake)
• Architect analytical solutions on Snowflake including: o Data modeling for analytics workloads o Performance optimization and cost efficiency o Workload separation and concurrency management
• Define best practices for: o Delta Lake / Iceberg / analytical table formats o Warehouse and cluster sizing strategies o Ingestion and transformation patterns o Caching and query optimization Classification: Internal Use
• Guide teams on: o Snowflake SQL, notebooks, and compute patterns o Snowflake virtual warehouses, clustering, and data sharing
• Design analytics architectures that support BI, ML, AI/NLQ and data science workloads on the same platform Governance, Quality & Security
• Implement analytics-focused governance on cloud data platforms: o Row-level and column-level security o Dynamic data masking o Secure data sharing mechanisms
• Define and monitor analytics data quality rules
• Ensure compliance with enterprise security, privacy, and regulatory standards
• Partner with data governance and platform security teams Collaboration & Leadership
• Collaborate closely with: o Data engineers o Analytics and BI teams o Data scientists o Enterprise and solution architects
• Provide architectural leadership and design governance
• Mentor teams on analytics modeling, platform usage, and best practices Required Skills & Experience Modern Data Stack (Mandatory)
• Hands-on architectural experience with: o Databricks and/or Snowflake in enterprise environments
• Strong understanding of: o Lakehouse vs warehouse architecture o Information catalogs on Snowfake and/or Databricks o Compute–storage separation o Cost and performance optimization for analytics workloads
• Experience enabling analytics use cases at scale on cloud platforms Classification: Internal Use Core Analytics & Data Skills
• Advanced analytical and dimensional data modeling expertise
• Strong SQL and query optimization skills
• Hands on experience of various modeling approaches – logical, physical and dimensional
• Experience building semantic layers and governed metrics
• Understanding of ETL, ELT patterns and analytics pipeline design
• Agile delivery method, process and toolings Cloud & Architecture
• Experience on an AWS cloud platform (or equivalent)
• Familiarity with: o Cloud native analytics services o Metadata management and data catalogs o Data observability and quality tooling Qualifications Education: Bachelor�s degree (any stream) Business & Analytical Acumen
• Ability to translate business requirements into scalable analytics designs
• Strong understanding of: o KPIs o Management and operational reporting
• Excellent communication, presentation, and stakeholder engagement skills Preferred Qualifications
• Industry experience in BFSI preferable
• Architecture or cloud certifications (nice to have)
• Experience 16–18 years overall experience 5–8+ years in data & analytics architecture, analytics engineering, or enterprise analytics roles 3+ years hands-on experience designing on modern cloud data platforms (Databricks, Snowflake, or equivalent) Role Summary The Senior Data & Analytics Architect designs, governs, and evolves the enterprise analytics architecture to enable trusted, scalable, and self-service decisioning. This role translates business outcomes into an actionable analytics architecture across modern cloud data platforms, ensuring data is consistent, governed, high-performing, and AI-ready. You will lead warehouse/Lakehouse architecture, data products, semantic layers, and metrics governance—partnering with engineering, BI, and data science to standardize patterns, develop best practices, architecture decisions and accelerate delivery. Success in this role is measured by faster time-to-insight, consistent KPI definitions across the enterprise, improved platform performance/cost, and increased reuse of governed data products.

Key Responsibilities Analytics & Data Architecture:

• Define and maintain the enterprise data and analytics architecture blueprints, vision, principles, and target states.
• Ensure data ecosystem alignment with enterprise architecture strategy, business capabilities, and operating models.
• Collaborate to manage, maintain, and mature the health of data and analytics capabilities for the enterprise
• Define and maintain the enterprise analytics architecture across modern data platforms including: o Analytical data models and marts o Semantic and metrics layers o Cloud data lake and warehouse solutions
• Design analytics solutions optimized for: o BI reporting o Self-service analytics o Advanced analytics and AI consumption Classification: Internal Use
• Ensure analytics architectures scale with growing data volumes and concurrent users
• Own the analytics architecture roadmap (target state, phased migration, and decommission plan) aligned to business priorities
• Define reference architectures, standards, and guardrails; serve as design authority for analytics modeling and consumption patterns Data Modeling & Semantic Layer
• Own analytical and dimensional data models for enterprise reporting
• Design and govern: o Conformed dimensions o Business metrics and KPIs o Semantic layers on top of Databricks / Snowflake
• Enable tool agnostic analytics consumption across multiple BI platforms
• Ensure consistency of definitions across dashboards, reports, and AI use cases Data Products & Marketplace Enablement
• Drive a data-as-a-product approach for analytical datasets, including product ownership, lifecycle management, and adoption
• Define analytics data products hosted on Databricks / Snowflake with clear onboarding, versioning, and support processes, including: o Clear schemas and contracts o Quality thresholds and SLAs o Documented usage patterns
• Enable discoverability, trust, and reuse through catalog and marketplace integration Modern Data Platform Architecture (Snowflake)
• Architect analytical solutions on Snowflake including: o Data modeling for analytics workloads o Performance optimization and cost efficiency o Workload separation and concurrency management
• Define best practices for: o Delta Lake / Iceberg / analytical table formats o Warehouse and cluster sizing strategies o Ingestion and transformation patterns o Caching and query optimization Classification: Internal Use
• Guide teams on: o Snowflake SQL, notebooks, and compute patterns o Snowflake virtual warehouses, clustering, and data sharing
• Design analytics architectures that support BI, ML, AI/NLQ and data science workloads on the same platform Governance, Quality & Security
• Implement analytics-focused governance on cloud data platforms: o Row-level and column-level security o Dynamic data masking o Secure data sharing mechanisms
• Define and monitor analytics data quality rules
• Ensure compliance with enterprise security, privacy, and regulatory standards
• Partner with data governance and platform security teams Collaboration & Leadership
• Collaborate closely with: o Data engineers o Analytics and BI teams o Data scientists o Enterprise and solution architects
• Provide architectural leadership and design governance
• Mentor teams on analytics modeling, platform usage, and best practices Required Skills & Experience Modern Data Stack (Mandatory)
• Hands-on architectural experience with: o Databricks and/or Snowflake in enterprise environments
• Strong understanding of: o Lakehouse vs warehouse architecture o Information catalogs on Snowfake and/or Databricks o Compute–storage separation o Cost and performance optimization for analytics workloads
• Experience enabling analytics use cases at scale on cloud platforms Classification: Internal Use Core Analytics & Data Skills
• Advanced analytical and dimensional data modeling expertise
• Strong SQL and query optimization skills
• Hands on experience of various modeling approaches – logical, physical and dimensional
• Experience building semantic layers and governed metrics
• Understanding of ETL, ELT patterns and analytics pipeline design
• Agile delivery method, process and toolings Cloud & Architecture
• Experience on an AWS cloud platform (or equivalent)
• Familiarity with: o Cloud native analytics services o Metadata management and data catalogs o Data observability and quality tooling
• Education: Bachelor�s degree (any stream) Business & Analytical Acumen
• Ability to translate business requirements into scalable analytics designs
• Strong understanding of: o KPIs o Management and operational reporting
• Excellent communication, presentation, and stakeholder engagement skills Preferred Qualifications
• Industry experience in BFSI preferable
• Architecture or cloud certifications (nice to have)