EXL—Snowflake COE Lead

Job description

Description: Key Responsibilities 1. COE Leadership & Strategy • Build and scale Snowflake CoE capabilities (architecture, engineering, governance, FinOps). • Define reference architectures, reusable assets, frameworks, and accelerators. • Drive capability development, hiring strategy, and upskilling initiatives. • Establish standards for delivery quality, security, and performance. 2. Solution Architecture & Advisory • Own end-to-end Snowflake architecture for enterprise-scale programs. • Translate business goals into data platform strategies (lakehouse / data warehouse / modern data stack). • Design solutions across: o Data ingestion, transformation, and orchestration o Data modelling (Dimensional, Data Vault, Medallion/Lakehouse architectures) o Data consumption (BI, APIs, AI/ML integration) • Lead architecture governance and design reviews. 3. Delivery Leadership & Governance • Lead large-scale implementations and transformations on Snowflake. • Define and track delivery KPIs, cost optimisation (FinOps), and performance metrics. • Ensure adoption of CI/CD, DataOps, and DevSecOps practices. • Act as escalation point for complex technical challenges. 4. Client Engagement & Consulting • Partner with clients to define data strategies, roadmaps, and transformation journeys. • Lead solution workshops, proposals, and executive discussions. • Provide thought leadership in Snowflake, Data Engineering, and Analytics. • Drive account growth through cross-selling and innovation-led proposals. 5. Engineering Excellence & Mentorship • Mentor architects and engineers across projects. • Build strong internal communities around: o Snowflake o dbt / modern data stack o Cloud-native data engineering • Promote best practices in coding, modelling, and governance. Must Have Skills & Experience • 15+ years overall experience in data engineering / analytics / data platform roles. • 7+ years in Snowflake with strong architecture + delivery experience. • Proven experience in leading CoE / practice / large delivery teams. • Deep expertise in: o Snowflake (Snowpipe, Streams & Tasks, Dynamic Tables, Secure Sharing) o Data modelling (Dimensional / Data Vault / Lakehouse) o Performance tuning & cost optimisation strategies o Data governance & security (RBAC, masking, row/column security) • Strong experience in ELT/ETL pipelines and orchestration tools (ADF, Airflow, dbt, Matillion, Informatica) • Hands-on experience in client-facing consulting roles and solution design. Good to Have • Experience on Azure / AWS / GCP ecosystems • Exposure to GenAI / AI integration with data platforms • Experience in domain analytics (Insurance, BFSI, Healthcare) • Familiarity with modern data stack tools (dbt, Python frameworks, Spark) Requirements: Key Responsibilities 1. COE Leadership & Strategy • Build and scale Snowflake CoE capabilities (architecture, engineering, governance, FinOps). • Define reference architectures, reusable assets, frameworks, and accelerators. • Drive capability development, hiring strategy, and upskilling initiatives. • Establish standards for delivery quality, security, and performance. 2. Solution Architecture & Advisory • Own end-to-end Snowflake architecture for enterprise-scale programs. • Translate business goals into data platform strategies (lakehouse / data warehouse / modern data stack). • Design solutions across: o Data ingestion, transformation, and orchestration o Data modelling (Dimensional, Data Vault, Medallion/Lakehouse architectures) o Data consumption (BI, APIs, AI/ML integration) • Lead architecture governance and design reviews. 3. Delivery Leadership & Governance • Lead large-scale implementations and transformations on Snowflake. • Define and track delivery KPIs, cost optimisation (FinOps), and performance metrics. • Ensure adoption of CI/CD, DataOps, and DevSecOps practices. • Act as escalation point for complex technical challenges. 4. Client Engagement & Consulting • Partner with clients to define data strategies, roadmaps, and transformation journeys. • Lead solution workshops, proposals, and executive discussions. • Provide thought leadership in Snowflake, Data Engineering, and Analytics. • Drive account growth through cross-selling and innovation-led proposals. 5. Engineering Excellence & Mentorship • Mentor architects and engineers across projects. • Build strong internal communities around: o Snowflake o dbt / modern data stack o Cloud-native data engineering • Promote best practices in coding, modelling, and governance. Must Have Skills & Experience • 15+ years overall experience in data engineering / analytics / data platform roles. • 7+ years in Snowflake with strong architecture + delivery experience. • Proven experience in leading CoE / practice / large delivery teams. • Deep expertise in: o Snowflake (Snowpipe, Streams & Tasks, Dynamic Tables, Secure Sharing) o Data modelling (Dimensional / Data Vault / Lakehouse) o Performance tuning & cost optimisation strategies o Data governance & security (RBAC, masking, row/column security) • Strong experience in ELT/ETL pipelines and orchestration tools (ADF, Airflow, dbt, Matillion, Informatica) • Hands-on experience in client-facing consulting roles and solution design. Good to Have • Experience on Azure / AWS / GCP ecosystems • Exposure to GenAI / AI integration with data platforms • Experience in domain analytics (Insurance, BFSI, Healthcare) • Familiarity with modern data stack tools (dbt, Python frameworks, Spark) Responsibilities: Key Responsibilities 1. COE Leadership & Strategy • Build and scale Snowflake CoE capabilities (architecture, engineering, governance, FinOps). • Define reference architectures, reusable assets, frameworks, and accelerators. • Drive capability development, hiring strategy, and upskilling initiatives. • Establish standards for delivery quality, security, and performance. 2. Solution Architecture & Advisory • Own end-to-end Snowflake architecture for enterprise-scale programs. • Translate business goals into data platform strategies (lakehouse / data warehouse / modern data stack). • Design solutions across: o Data ingestion, transformation, and orchestration o Data modelling (Dimensional, Data Vault, Medallion/Lakehouse architectures) o Data consumption (BI, APIs, AI/ML integration) • Lead architecture governance and design reviews. 3. Delivery Leadership & Governance • Lead large-scale implementations and transformations on Snowflake. • Define and track delivery KPIs, cost optimisation (FinOps), and performance metrics. • Ensure adoption of CI/CD, DataOps, and DevSecOps practices. • Act as escalation point for complex technical challenges. 4. Client Engagement & Consulting • Partner with clients to define data strategies, roadmaps, and transformation journeys. • Lead solution workshops, proposals, and executive discussions. • Provide thought leadership in Snowflake, Data Engineering, and Analytics. • Drive account growth through cross-selling and innovation-led proposals. 5. Engineering Excellence & Mentorship • Mentor architects and engineers across projects. • Build strong internal communities around: o Snowflake o dbt / modern data stack o Cloud-native data engineering • Promote best practices in coding, modelling, and governance. Must Have Skills & Experience • 15+ years overall experience in data engineering / analytics / data platform roles. • 7+ years in Snowflake with strong architecture + delivery experience. • Proven experience in leading CoE / practice / large delivery teams. • Deep expertise in: o Snowflake (Snowpipe, Streams & Tasks, Dynamic Tables, Secure Sharing) o Data modelling (Dimensional / Data Vault / Lakehouse) o Performance tuning & cost optimisation strategies o Data governance & security (RBAC, masking, row/column security) • Strong experience in ELT/ETL pipelines and orchestration tools (ADF, Airflow, dbt, Matillion, Informatica) • Hands-on experience in client-facing consulting roles and solution design. Good to Have • Experience on Azure / AWS / GCP ecosystems • Exposure to GenAI / AI integration with data platforms • Experience in domain analytics (Insurance, BFSI, Healthcare) • Familiarity with modern data stack tools (dbt, Python frameworks, Spark)