Engineering Leader, Google COE

It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner. Please visit  Fractal | Intelligence for Imagination  for more information about Fractal. Role Overview We are seeking a highly experienced Engineering Lead with deep expertise across both Google Cloud Platform (GCP) and Google's internal First-Party (1P) Engineering Ecosystem . This role goes beyond solution architecture. The ideal candidate will be a hands-on technical leader capable of driving architecture, engineering execution, delivery governance, reliability, and innovation across large-scale enterprise programs. You will serve as the technical authority for clients while mentoring engineering teams and ensuring delivery excellence. The ideal candidate will have: 10+ years of software engineering and distributed systems experience. Strong hands-on expertise building large-scale platforms using GCP. Prior Google experience or substantial exposure to Google's 1P ecosystem. Experience leading teams as a Technical Lead (TL) or Engineering Lead. Deep understanding of distributed systems, data platforms, AI/ML systems, and cloud-native architectures. Proven ability to bridge technical strategy with hands-on execution. Key Responsibilities: Technical Architecture & Vision Define long-term technology roadmaps aligned with business objectives. Lead architecture and design for data, analytics, AI/ML, and platform engineering initiatives. Design scalable and resilient systems leveraging: BigQuery Vertex AI Dataflow Dataproc Cloud Composer Event-driven and microservices architectures Drive engineering standards, code quality, platform governance, and architectural consistency. Make critical technology decisions considering scalability, reliability, maintainability, and cost efficiency. Translate business requirements into technical specifications and executable engineering plans. First-Party Google Engineering Expertise Candidates with prior Google experience should demonstrate deep working knowledge of Google's internal engineering ecosystem, including: Compute, Serving & Deployment Strong understanding of Borg and large-scale cluster orchestration principles. Experience with Google's service deployment ecosystem including Boq , Pods, and microservice-based architectures. Knowledge of capacity planning, resource management, rollout strategies, and production deployment processes. Storage & Data Infrastructure Experience designing systems leveraging: Spanner Colossus (CNS) Placer Understanding of data consistency, replication, distributed transactions, and geo-distributed storage systems. Development Ecosystem Deep familiarity with: google3 CitC (Clients in the Cloud) Monorepo development practices Experience managing complex dependency structures and large-scale codebases. Strong working knowledge of: Blaze BUILD files Dependency management Build optimization Engineering Excellence Experience with Google's: Code review culture Critique Readability standards Ability to establish engineering excellence through code quality, testing, and design reviews. Serve as the technical quality bar for the program. Observability & Reliability Experience with reliability engineering practices. Familiarity with: Monarch Dapper Production monitoring Distributed tracing Service health management AI-Assisted Engineering Familiarity with Google internal AI development tooling and developer productivity systems. Experience using AI-assisted development practices to improve engineering velocity and quality. Exposure to tools such as JetSki and modern GenAI-enabled software development workflows is preferred. Delivery Leadership Own end-to-end delivery of strategic client programs. Ensure successful execution across architecture, engineering, testing, deployment, and production support. Lead distributed teams across multiple geographies. Drive governance, risk mitigation, dependency management, and stakeholder communication. Step into hands-on engineering during critical program phases when necessary. Balance short-term delivery goals with long-term platform sustainability. Client Leadership Serve as trusted technical advisor to senior client stakeholders. Lead architecture reviews, strategy discussions, and executive presentations. Translate complex technical concepts for both engineering and business audiences. Identify opportunities to drive innovation and business impact through technology. Technical Leadership & Team Development A successful Engineering Lead will demonstrate: Technical Vision Convert ambiguous business problems into executable technical solutions. Create clear engineering roadmaps and architectural direction. Team Enablement Identify roadblocks before they impact delivery. Manage dependencies across engineering, product, security, infrastructure, and platform teams. Drive alignment and execution across multiple stakeholders. Mentorship & Talent Development Mentor engineers and technical leads. Delegate effectively while maintaining accountability. Foster a culture of technical excellence, learning, and innovation. Help engineers grow through coaching, design reviews,