Engineering Manager

Role Summary Lifesight is looking for an Engineering Manager to lead engineering teams building our marketing measurement and decision intelligence platform. This role is suited for a hands-on engineering leader who can manage high performing engineers, guide architecture, improve execution discipline, and build scalable SaaS systems for enterprise customers. The role will involve leading product engineering across marketing data pipelines, measurement workflows, attribution systems, MMM output consumption, experimentation modules, customer dashboards, reporting layers, AI-assisted insights, platform integrations, and production-grade SaaS infrastructure . The ideal candidate should be comfortable operating at the intersection of product engineering, data systems, cloud infrastructure, reliability, security, and marketing technology . Key Responsibilities The Engineering Manager will lead one or more engineering squads responsible for building scalable and reliable product capabilities for Lifesight s marketing measurement platform. The person will work closely with Product, Data Science, Marketing Science, Solutions, Customer Success, and GTM teams to convert complex customer and measurement requirements into clear engineering roadmaps and production-ready features. The role will involve owning architecture decisions for backend services, APIs, data workflows, integrations, dashboards, model-output consumption layers, and customer-facing product modules. The person should ensure that engineering solutions are scalable, secure, maintainable, observable, and aligned with product requirements. Champion AI Adoption: Act as the evangelist for AI-augmented development, driving the deep integration of tools like Claude Code, Cursor, Antigarvity 2.0, and custom internal agents. The role will also require close partnership with implementation and solutions teams. Since Lifesight works with fragmented customer data across multiple sources, the Engineering Manager should understand data quality issues such as schema mismatches, type errors, data transformation gaps, API failures, and integration inconsistencies. This will help the team build stronger onboarding, data validation, and platform configuration workflows. Required Skills and Experience The candidate should have 8 - 12 years of engineering experience , including 2 - 4 years of experience managing engineering teams in a product or SaaS environment. Strong hands-on experience is expected in backend or full-stack product engineering using Java , Python, TypeScript, JavaScript or similar languages . The candidate should have strong working knowledge of modern distributed systems, and modern web application infrastructure . The role requires experience with cloud platforms such as GCP or AWS The candidate should understand production-readiness practices such as SLOs, incident response, root cause analysis, postmortems, backup and restore testing, capacity planning, performance tuning, and disaster recovery . Experience with BigQuery, Google Cloud Platform, data transformation workflows, marketing platform integrations, ad-platform APIs, customer data platforms, tag managers, SDKs, pixels, or attribution systems would be valuable . Domain Exposure Prior exposure to MarTech, AdTech, marketing analytics, attribution, incrementality testing, Marketing Mix Modelling, experimentation platforms, customer data platforms, or marketing measurement products would be a strong advantage. The candidate does not need to be a data scientist, but should be able to work effectively with data scientists and marketing science teams. The person should understand how statistical or model outputs need to be converted into reliable, explainable, and usable product experiences for business users. Leadership Expectations The Engineering Manager should be able to coach engineers, review architecture, unblock execution, and create clarity in a fast-moving SaaS environment. The person should balance speed with engineering discipline and ensure that the team builds systems that are not only functional, but also reliable, secure, observable, and scalable. The role requires someone who can work with ambiguity, ask the right technical and product questions, and convert complex business problems into structured engineering solutions. Success Measures Success in this role will be measured through improved engineering delivery predictability, reduction in production issues, stronger release readiness, scalable platform architecture, improved data pipeline reliability, faster customer onboarding support, better observability, reduced operational toil, and successful delivery of product capabilities across marketing measurement, attribution, experimentation, and AI-led decision intelligence. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.