Senior Engineering Manager, Data & Cloud Platform

At Northwind Robotics , we are a team of innovators focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available. For over 35 years, the experts at Northwind Robotics have been focused on solving the complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries. As we continue to grow, we’re always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Northwind Robotics a truly exceptional place to work! About the AI Platform Organization Northwind Robotics ’s AI mission is to turn its legal data and products into a unified, safe AI platform that powers the top legal workflows in every core solution and drives meaningful gains in legal team productivity and AI-influenced revenue. The organization is designed using Team Topologies: stream-aligned product teams sit at the top, an AI Platform layer sits in the middle, and Cloud & Data Foundation sits below. This role sits at the foundation layer and builds the infrastructure and data capabilities the rest of the AI organization depends on. About the Role This role leads the Data & Cloud Platform function for the AI organization. It is accountable for the data and infrastructure foundation that makes higher-level AI platform capabilities and product experiences possible. The role blends cloud platform engineering with strong data responsibilities: building a unified access layer across fragmented product data stores, shaping how shared agent memory and context systems should work, operatingthe cloud foundations underneath the AI platform, and helping execute the broader data strategy. This is explicitly a hands-on leadership role. The team begins small — likely one to two direct reports — and you are expected to spend roughly half of your time writing and reviewing production code while the organization scales. What You Will Do Own the cloud and data foundation for the AI organization — shared runtime infrastructure, CI/CD patterns, and platform services used by AI teams Design and build a unified data access layer for agents and AI features so they can interact with a coherent interface rather than bespoke storage and retrieval systems in each product Define where shared AI data should live — agent memory, context stores, and shared service datasets — while partnering with product teams that have compliance or customer-specific reasons to retain specialized storage patterns Build and operate data ingestion and transformation pipelines that support AI workloads and analytics, including ETL and lakehouse-style patterns where needed Shape the technical execution of the organization’s data strategy, especially around scale, operability, cost, and long-term maintainability Partner with the AI Quality & Governance team to support privacy-sensitive data handling, PII-aware workflows, and safe access patterns for observability, memory, and trace data Provide platform patterns, reference architectures, and reusable services that help product teams migrate toward shared foundations instead of building fragmented point solutions Hire, mentor, and grow the team over time — the function may later mature into separate data-platform and cloud-platform groups What We Are Looking For You have 8+ years of software engineering experience including meaningful experience leading engineers as a manager or technical lead. You have built and operated shared platform and data systems in production across multiple teams. Strong experience with cloud infrastructure, distributed systems, developer platforms, or internal platform engineering Hands-on experience building or operating shared data systems: ETL/ELT pipelines, internal data services, warehouse or lakehouse environments, or other multi-team data platforms Experience designing platform APIs or integration layers that make complex infrastructure or fragmented systems easier for other teams to consume Demonstrated exposure to production AI systems, especially where data architecture and infrastructure materially affect quality, reliability, latency, or cost Comfort operating in ambiguous environments where the architecture is evolving and the right answer may be a combination of shared services, integration layers, and selective platform consolidation A strong bias toward hands-on execution, systems thinking, and building scalable foundations that other teams can trust Experience with agent memory, retrieval systems, semantic search,