NTT DATA is seeking a hands-on Senior Snowflake Engineer to provide senior technical leadership across enterprise Snowflake implementations. The role combines deep Snowflake engineering expertise with proven delivery of complex, secure, scalable, performant and cost-effective solutions into production. 

Working as a technical peer to Data Architects, Solution Architects and delivery teams, the engineer will shape implementation decisions, lead critical workstreams, resolve complex engineering challenges and strengthen delivery standards. This is not a management or theory-led architecture role. The successful candidate must remain close to the technology and take direct ownership where complexity, delivery risk or engineering quality requires senior intervention. 

Key Responsibilities 

• Lead the engineering of enterprise Snowflake data platforms and data products from design through deployment, stabilisation and optimisation. 

• Take hands-on ownership of technically complex or high-risk implementation components and lead Snowflake technical workstreams. 

• Contribute implementation expertise to solution design and evaluate decisions for scalability, performance, security, maintainability, operational supportability and cost. 

• Define and apply engineering standards, proven implementation patterns, reusable reference solutions and accelerators. 

• Design and implement appropriate batch, near real-time and real-time ingestion, transformation and processing patterns. 

• Resolve complex issues across ingestion, transformation, modelling, integration, SQL workloads, warehouse configuration, compute utilisation, security and platform operations. 

• Drive performance tuning and cost optimisation without compromising reliability, scalability or delivery outcomes. 

• Implement appropriate security, access and governance controls for sensitive and regulated data. 

• Review code, configurations and implementation approaches for quality, maintainability, production readiness and technical assurance. 

• Support deployment, production stabilisation and complex troubleshooting where senior Snowflake expertise is required. 

• Guide delivery teams through practical technical leadership, constructive challenge, knowledge transfer and sound trade-off decisions grounded in production experience. 

• Contribute to the development and consistency of Snowflake engineering capability across the wider Data & AI practice. 

Knowledge and Attributes 

The successful candidate should demonstrate strong practical knowledge across the following areas: 

• Snowflake platform architecture and configuration, Snowpipe, Streams and Tasks, Dynamic Tables, stored procedures, Snowflake Scripting and advanced SQL. 

• Query optimisation, performance tuning, warehouse sizing, workload management, compute and storage optimisation, cost management, monitoring and troubleshooting. 

• Enterprise data engineering, including ELT/ETL, ingestion, batch and streaming patterns, CDC, transformation frameworks, orchestration, data quality, error recovery and operational support. 

• Enterprise data warehouses, modern data platforms, data lakes or lakehouses, data products, dimensional and analytical modelling, and integration across multiple source systems. 

• Snowflake security and governance, including RBAC, Secure Views, Secure Data Sharing, access controls, environment separation and production access management. 

• Modern engineering practices, including cloud platforms such as AWS, Microsoft Azure or Google Cloud, Infrastructure as Code, CI/CD, Git workflows, observability, APIs, event-driven integration, and enterprise security and identity platforms. 

• Practical exposure to Snowpipe Streaming, Snowpark, Python within Snowflake, Apache Iceberg, Snowflake Marketplace, Native Applications or Cortex AI is advantageous. Candidates are not expected to specialise in every capability. Greater weight will be given to meaningful production experience, appropriate technology choices and an ability to explain implementation trade-offs. 

Required professional attributes include: 

• Strong technical judgement, analytical thinking and systematic problem-solving grounded in practical delivery. 

• The credibility to operate as a senior engineering authority and challenge assumptions constructively. 

• Clear communication and effective collaboration with architects, engineers, delivery stakeholders and client teams. 

• A pragmatic ability to balance engineering quality, scalability, performance, security, cost and delivery constraints. 

• A strong focus on production readiness, operational quality, maintainability and measurable delivery outcomes. 

• A willingness to remain hands-on and guide engineers through practical technical leadership and knowledge transfer. 

Qualifications and Certifications 

• Relevant SnowPro certifications are desirable evidence of platform knowledge but are not a substitute for successful implementation experience. 

• Deep production experience should not be excluded solely because a candidate does not hold formal Snowflake certifications. 

• A relevant qualification in data engineering, computer science, software engineering or a related technical discipline is advantageous. 

Required Experience 

Candidates will be assessed primarily on the depth, complexity, scale and quality of what they have successfully implemented and delivered, rather than years of experience or exposure to Snowflake alone. 

• Successful delivery of multiple enterprise-scale Snowflake implementations into production. 

• Substantial hands-on involvement across solution design, engineering, deployment, operationalisation, stabilisation and ongoing optimisation. 

• Delivery of enterprise-grade data platforms and/or data products on Snowflake. 

• Experience as the senior technical lead, Senior Engineer, Lead Engineer or equivalent on complex Snowflake workstreams. 

• Strong SQL and data engineering capability, with proven ability to diagnose and resolve complex implementation, integration, performance and production issues. 

• Experience making and explaining technical trade-offs relating to scalability, performance, security, maintainability, operational complexity and cost. 

• Collaboration with Data Architects, Solution Architects and engineering teams to turn solution designs into reliable production implementations. 

• Demonstrated ownership of meaningful production outcomes, including the ability to explain decisions made, why approaches were selected and what was learned through delivery.