• Serve as a domain authority for nVent Data Solutions products, including their application, for nVent functions • Provide first-line product support by providing help to customers and colleagues using nVent Data Solutions and Electrical Connections • Evaluate customer RFQs and build costing/quotes with technical and commercial content • Provide technical application assistance to identify modified and engineered products based on customer specifications and application requirements • Prepare and submit quotations & proposals of non-standard products using internal CPQ and ERP systems • Conduct product training for customers and train distributors on quoting tools • Prepare compliance documents against customer specifications • Lead strategic projects for product improvements, cost reduction, product consolidation, and new product development • Review training literature for technical accuracy including installation guides and procedures, operations manuals, and online learning • Represent nVent in industry codes and standards committees • Provide market-specific intelligence and feedback on products and services to the commercial teams • Assist commercial teams with voice-of-customer (VOC) visits • Plan activities together with the Sales Leader • Identify the project in the closing phase with the sales team and provide vital technical support • Advises their manager about all situations, developments, and occurrences that are significant for the manager or are requested by them

• Develop and implement scalable and performant solutions. • Partner, as a peer, with Engineering Managers, Product Managers, and stakeholders throughout Aledade to develop and execute technical roadmaps using Agile processes. • Mentor and coach more junior engineers including thorough pull request reviews for other developers and be receptive to critical feedback on your own work.

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost. • Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams. • Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement. • Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics. • Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations. • Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency. • Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture. • Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure. • Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost. • Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams. • Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement. • Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics. • Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations. • Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency. • Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture. • Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure. • Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.