Technical Lead, Data Platform as a Service (DPaaS) — Google Cloud - US Citizen or Green Card Only
Revolution Technologies · Houston, TX
Apply & track with Apply EdgeTechnical Lead, Data Platform as a Service (DPaaS) — Google Cloud
The platform provides standardized, secure, self-service capabilities for data ingestion, storage, processing, orchestration, governance, observability, security, data sharing, and cost management.This is a hands-on technical leadership position requiring deep expertise in modern data platforms, cloud architecture, software engineering, infrastructure as code, and emerging agentic AI capabilities.The Technical Lead will establish platform architecture and engineering standards while remaining actively involved in solution design, development, architecture decisions, code and design reviews, and technical problem-solving.A major focus of the role will be building a unified DPaaS control plane and advancing the platform toward AI-driven and agentic operations, enabling intelligent provisioning, monitoring, optimization, incident diagnosis, and platform management.Key ResponsibilitiesPlatform Architecture & Technical LeadershipDefine and evolve the enterprise DPaaS architecture on Google Cloud, encompassing data ingestion, lakehouse storage, processing, orchestration, governance, observability, and data serving.Establish technical roadmaps, engineering standards, reusable architectures, and platform design patterns.Author and review RFCs and Architecture Decision Records (ADRs) addressing scalability, interoperability, security, reliability, operability, and total cost of ownership.Lead proofs of concept and technical evaluations as new Google Cloud data and AI capabilities become available.Provide technical direction and mentorship across data, platform, and full-stack engineering teams.Platform Engineering & Lifecycle ManagementOwn platform capabilities throughout their lifecycle, including discovery, architecture, development, deployment, operation, versioning, modernization, and deprecation.Implement infrastructure as code using Terraform, including reusable modules, state management, and policy-as-code practices.Establish GitOps and CI/CD practices using technologies such as Cloud Build and GitHub Actions.Standardize testing, environment promotion, deployment, and release processes across development, test, and production environments.Design scalable multi-tenant architectures incorporating project/folder hierarchies, tenant isolation, quotas, and automated onboarding/offboarding.DPaaS Control Plane & Full-Stack EngineeringArchitect and build a unified DPaaS control plane providing teams with a single interface for requesting, configuring, monitoring, and managing data resources.Develop backend services and APIs using Python, Go, or Java.Build cloud-native services using Cloud Run and GKE that orchestrate Terraform, Google Cloud APIs, and platform workflows.Develop modern user experiences using TypeScript and frameworks such as React.Enable self-service capabilities for infrastructure provisioning, data catalog discovery, access requests, platform health, and cost visibility.Treat the platform as an internal product and measure adoption, user experience, and time-to-first-pipeline.Agentic AI & Intelligent Platform OperationsDesign and implement AI agents and LLM-powered applications that automate and enhance platform operations.Build intelligent agents capable of provisioning infrastructure, monitoring platform health and data quality, diagnosing incidents, and recommending or executing cost, capacity, and performance optimizations.Develop AI digital twins for platform engineers that incorporate operational runbooks, architecture knowledge, and engineering practices.Explore digital-twin and simulation approaches for modeling platform configurations and workloads, forecasting capacity, and evaluating changes before production deployment.Establish appropriate AI governance and guardrails, including human-in-the-loop approvals, policy enforcement, audit trails, evaluation frameworks, and agent safety controls.Leverage technologies such as Vertex AI, Gemini, Agent Development Kit (ADK), Model Context Protocol (MCP), and multi-agent orchestration patterns.Reliability, Security & Data GovernanceEstablish platform SLIs/SLOs, monitoring, alerting, incident response, failure recovery, and operational reliability standards.Implement observability using Google Cloud Monitoring and Cloud Logging.Embed security into platform architecture using IAM, least-privilege access, VPC Service Controls, Cloud KMS/CMEK, Sensitive Data Protection, and audit logging.Lead data governance, lineage, cataloging, and data-quality capabilities using Dataplex Universal Catalog.Partner with security, risk, architecture, and compliance teams to meet applicable regulatory and enterprise requirements.FinOps, Performance & OptimizationOwn platform-level Data FinOps and cost optimization practices.Develop strategies for BigQuery Editions and reservations, workload management, cost attribution, tenant-level consumption reporting, budgets, and cost alerts.Optimize storage, query, compute, and processing workloads operating at enterprise scale.Establish visibility into platform consumption and drive ongoing improvements in cost efficiency, performance, and capacity utilization.Team Leadership & CollaborationProvide technical leadership, mentorship, and engineering guidance to data, platform, cloud, and full-stack engineers.Raise engineering standards through architecture reviews, design reviews, code reviews, reusable patterns, and technical mentoring.Partner closely with enterprise architecture, cybersecurity, product, infrastructure, AI/ML, and consuming data teams.Communicate complex technical strategies effectively to both engineering teams and executive stakeholders.Required QualificationsU.S. citizen or Green Card Only8+ years of experience in software engineering, data engineering, cloud engineering, or platform engineering, with 2+ years in a technical leadership capacity such as Technical Lead, Staff Engineer, or Principal Engineer.Demonstrated ownership of a production-scale data or cloud platform from architecture and engineering through deployment, operations, and enterprise adoption.Strong experience with Google Cloud Platform (GCP) and modern cloud/data-platform architecture.Advanced expertise with Terraform and Infrastructure as Code, including reusable modules, state management, automation, and policy as code.Strong full-stack software engineering capabilities, including:Python, Go, or JavaREST/API and backend service developmentTypeScript and React or comparable modern front-end frameworksHands-on experience developing agentic AI, Generative AI, or LLM-powered applications in production environments.Experience with Vertex AI, Gemini, or comparable enterprise AI platforms.Strong cloud-native and container experience with Cloud Run, Kubernetes, and GKE.Working knowledge of GCP security and governance technologies, including IAM, VPC Service Controls, CMEK/Cloud KMS, and Dataplex.Advanced SQL skills and strong knowledge of data modeling, distributed systems, and enterprise data architecture.Demonstrated ability to influence technical direction across teams without relying on direct authority.Excellent technical communication, collaboration, and leadership skills.Preferred QualificationsExperience with one or more of the following is highly desirable:BigQueryGoogle Cloud StorageBigLakeDataflow / Apache BeamDataproc / Dataproc ServerlessPub/SubDatastreamCloud Composer / Apache AirflowVertex AI Agent EngineAgent Development Kit (ADK)Model Context Protocol (MCP)Multi-agent architectures and orchestrationInternal developer platforms or portals such as BackstageDigital twins, infrastructure simulation, or AIOpsApache Iceberg and open table formatsBigQuery Analytics HubSpanner, AlloyDB, or BigtableData observability and data-quality platformsMedallion/lakehouse architectureEnterprise Data FinOps and cloud cost optimizationGoogle Cloud Professional certifications are preferred.Ideal CandidateThe ideal candidate combines the architecture depth of a Principal or Staff Engineer with the hands-on engineering ability to build production systems. This individual understands how to create a scalable enterprise data platform, automate infrastructure and operations, establish strong governance and reliability practices, and use emerging agentic AI capabilities to transform how cloud data platforms are provisioned, operated, monitored, and optimized.This is an opportunity to shape the architecture and technical direction of an enterprise Google Cloud data platform while building the next generation of self-service and AI-enabled platform engineering capabilities.Equal Opportunity EmployerWe are an Equal Opportunity Employer and are committed to providing equal employment opportunities to all qualified applicants and employees without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable federal, state, or local law.We are committed to fostering an inclusive workplace where individuals are respected, valued, and provided the opportunity to succeed.