Engineering Leader, Google COE

Fractal Analytics•California, CA

About The Position

Fractal is seeking an experienced Engineering Lead with expertise in Google Cloud Platform (GCP) and Google's internal First-Party (1P) engineering ecosystem. This hands-on technical leadership role spans architecture, engineering execution, delivery governance, reliability, and innovation across large-scale enterprise programs. The Engineering Lead serves as a technical authority for clients, mentors engineering teams, and ensures delivery excellence. The role leads architecture and engineering for data, analytics, AI/ML, and platform initiatives, translating business needs into technical plans and setting roadmaps, engineering standards, and architectural direction. It also involves end-to-end program delivery, including governance, risk and dependency management, execution across distributed teams, deployment, and production support. The successful candidate advises senior client stakeholders, leads technical and executive discussions, and helps teams deliver sustainable platforms while stepping into hands-on engineering when needed. Candidates should bring substantial software, data, or platform engineering experience; proven technical leadership; enterprise distributed-systems delivery experience; and hands-on expertise with GCP services and relevant programming, data, DevOps, and MLOps technologies. Prior Google engineering experience or significant exposure to the Google 1P ecosystem is strongly preferred. The stated starting base salary estimate is $225,000. A discretionary bonus may also be available. Full-time employees are eligible for health, dental, vision, life, and disability insurance, 401(k) participation after 30 days, 11 paid holidays, 12 weeks of parental leave, and a flexible “free time” PTO policy.

Requirements

At least 10 years of software engineering, data engineering, or platform engineering experience.
Proven leadership as an Engineering Lead, Technical Lead, Staff Engineer, or Principal Engineer.
Experience delivering enterprise-scale distributed systems.
Hands-on expertise with Google Cloud services including BigQuery, Dataflow, Vertex AI, Dataproc, Cloud Composer, GKE, Cloud Run, and serverless technologies.
Strong proficiency in Python and Go.
Experience with SQL, PLX, Dremel, Spanner, and distributed data platforms.
Experience with CI/CD pipelines, Infrastructure as Code, containerization, Kubernetes, automated testing, and MLOps platforms.
Deep understanding of distributed systems, data platforms, AI/ML systems, and cloud-native architectures.
Ability to bridge technical strategy with hands-on execution.
For candidates claiming prior Google experience, deep working knowledge of Google's internal engineering ecosystem, including compute and deployment, storage and data infrastructure, development ecosystem, engineering excellence, and observability and reliability practices.
Understanding of Borg and large-scale cluster orchestration principles; service deployment ecosystems including Boq and Pods; capacity planning, resource management, rollout strategies, and production deployments.
Experience designing systems leveraging Spanner, Colossus (CNS), and Placer, with understanding of consistency, replication, distributed transactions, and geo-distributed storage.
Familiarity with google3, CitC, monorepo development, complex dependency structures, large codebases, Blaze, BUILD files, dependency management, and build optimization.
Experience with code review practices, Critique, Readability standards, and establishing engineering quality through reviews and testing.
Experience with reliability engineering, Monarch, Dapper, production monitoring, distributed tracing, and service health management.

Nice To Haves

Prior Google engineering experience.
Experience building and operating systems inside Google's First-Party engineering ecosystem.
Exposure to Borg, Spanner, Blaze, Monarch, Dapper, google3, Critique, and related internal tooling.
Familiarity with Google's internal AI development tooling and developer productivity systems.
Experience using AI-assisted development practices to improve engineering velocity and quality.
Exposure to JetSki and modern GenAI-enabled software development workflows.

Responsibilities

Define long-term technology roadmaps aligned with business objectives.
Lead architecture and design for data, analytics, AI/ML, and platform engineering initiatives.
Drive engineering standards, code quality, platform governance, and architectural consistency.
Make technology decisions with attention to scalability, reliability, maintainability, and cost efficiency.
Translate business requirements into technical specifications and executable engineering plans.
Own end-to-end delivery of strategic client programs across architecture, engineering, testing, deployment, and production support.
Lead distributed teams across multiple geographies and 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.
Serve as a trusted technical advisor to senior client stakeholders.
Lead architecture reviews, strategy discussions, and executive presentations.
Explain complex technical concepts to engineering and business audiences.
Identify opportunities to drive innovation and business impact through technology.
Convert ambiguous business problems into executable technical solutions and create engineering roadmaps and architectural direction.
Identify roadblocks and manage dependencies across engineering, product, security, infrastructure, and platform teams.
Drive alignment and execution across stakeholders.
Mentor engineers and technical leads, delegate effectively, and maintain accountability.
Foster technical excellence, learning, and innovation through coaching, design reviews, and stretch opportunities.
Establish engineering quality through code quality, testing, design reviews, and reliability practices.