Senior Platform Engineer - Data & Analytics Platforms

natgridProd•Waltham, MA

23h•$102,000 - $143,000•Hybrid

About The Position

As Senior Platform Engineer, you'll be responsible for designing, engineering, automating, and operating enterprise data and analytics platform capabilities that enable secure, scalable, and reliable data products, analytics solutions, and AI-enabled workloads. You'll provide broad platform engineering capability across National Grid’s strategic data and analytics ecosystem, with emphasis on Snowflake engineering, Azure administration, platform automation, Microsoft Fabric enablement, governance, reliability, and operational support. You'll develop reusable patterns and self-service capabilities that improve consistency, reduce manual effort, and make platform services easier for teams to adopt and operate.

Requirements

Experience in platform engineering, data platform engineering, cloud engineering, infrastructure engineering, or a DevOps-oriented role.
Hands-on experience engineering, administering, or operating Snowflake in an enterprise environment.
Strong understanding of Snowflake concepts including roles and grants, databases and schemas, warehouses, resource monitors, tasks and streams, data sharing, marketplace patterns, cloning, security controls, and workload management.
Experience with Snowflake security and governance patterns, including role-based access control, secure sharing, masking or access policies, auditability, and controlled access to sensitive data.
Experience optimizing Snowflake performance, reliability, and cost through query analysis, warehouse sizing, workload monitoring, and operational tuning.
Experience administering or engineering Microsoft Azure services, including identity integration, networking, private connectivity, monitoring, and production support considerations.
Experience with Microsoft Fabric, Power BI, or similar analytics platforms, including platform administration, governance, capacity or workspace management, and operational support.
Hands-on experience with automation, CI/CD, Infrastructure-as-Code, version control, testing, documentation, and repeatable deployment practices.
Experience integrating data platforms with ingestion, transformation, orchestration, catalog, BI, analytics, IT service management, and monitoring tools.
Experience supporting production platforms with observability, incident response, root cause analysis, runbooks, resilience, and continuous improvement.
Proficiency in SQL and at least one programming or scripting language used in platform engineering, such as Python, PowerShell, Java, Scala, or similar.
Ability to collaborate across engineering, architecture, security, governance, operations, vendor, and business-facing teams and communicate technical decisions clearly.

Responsibilities

Design, build, configure, and support enterprise data and analytics platform capabilities across Snowflake, Microsoft Fabric, Azure, Power BI, and associated services.
Engineer and administer Snowflake capabilities including databases, schemas, roles, grants, warehouses, resource monitors, tasks, streams, secure data sharing, marketplace patterns, workload management, and operational controls.
Develop reusable Snowflake patterns for onboarding, access management, data publication and consumption, workload isolation, environment configuration, and governed cross-business-unit data sharing.
Improve Snowflake performance and cost efficiency through query analysis, warehouse design, monitoring, resource management, usage analysis, and FinOps practices.
Build and maintain platform automation, CI/CD pipelines, Infrastructure-as-Code, scripts, templates, and repeatable deployment patterns that reduce manual or ticket-driven administration.
Support Azure administration and platform integration, including identity, networking, private connectivity, monitoring, security alignment, and configuration of cloud services used by data platforms.
Support Microsoft Fabric and Power BI platform capabilities, including capacity and workspace administration, connectivity, governance controls, monitoring, operational readiness, and adoption patterns.
Embed governance, security, auditability, data quality, observability, and cost-management controls directly into platform capabilities and engineering patterns.
Support production services through monitoring, alerting, incident response, root cause analysis, runbooks, service ownership, resilience planning, and continuous improvement.
Partner with data engineering, architecture, security, cloud, governance, operations, product, and business-aligned teams to translate requirements into secure and supportable platform solutions.
Contribute to platform roadmaps and standards by identifying opportunities to modernize services, improve developer and consumer experience, enable governed self-service, and support advanced analytics and AI-enabled use cases.