Technical Delivery Lead - Data & Agentic Transformation, Google Cloud

Accenture•Kirkland, WA

1d•$80,400 - $266,300•Hybrid

About The Position

We're the Accenture Google Business Group Mid-Market team, a specialized group of engineers, architects, and builders delivering tailored Google Cloud solutions for mid-market, high-growth, and fast-paced enterprises. We partner with dynamic organizations to connect them with the right combination of Accenture solutions and Google Cloud products to accelerate business transformation and solve real-world problems, quickly. We're expanding our team to meet the unique, rapid execution needs of the mid-market segment. The Domain Data and AI are no longer back-office functions — they're the engine of every modern business decision. And with the arrival of agentic AI, we're entering a new era: intelligent agents that don't just analyze data but act on it — automating complex workflows, making autonomous decisions, and transforming how companies operate. Google Cloud is leading this shift with the Gemini Enterprise Agent Platform, Vertex AI, and the most powerful data stack in the industry. Our mid-market clients need lean, agile leaders who can unlock massive amounts of untapped data and rapidly turn it into a distinct competitive advantage. As a Technical Delivery Lead, you own delivery from initial solution shaping through go-live. You design the execution approach for data and AI engagements, lead the implementation, and ensure the final product fundamentally upgrades how the client leverages data and intelligent automation. You're supported by a dedicated offshore engineering team — you set the strategic direction, and they build alongside you. But you're the single point of accountability for what ships. This role is for an execution-focused leader who takes full ownership of their work. You care deeply about delivery quality, data architecture outcomes, agent design, and speed-to-value for the business. You hold yourself to a higher standard than anyone else would. You'll work directly with client stakeholders — immersing yourself in their environment, understanding their fast-evolving resource constraints, and delivering outcomes that matter in weeks, not quarters. You'll have the autonomy to shape how they experience the full power of Google Cloud's data and AI stack, from first conversation to go-live.

Requirements

Minimum 7 years in hands-on, client-facing technology roles — you've built and delivered, not just managed.
Minimum 5 years architecting and delivering on Google Cloud Platform, with deep expertise in data engineering, analytics, AI/ML, and modern data platforms.
Independently owned client engagements from technical design through go-live.
Deep hands-on proficiency with BigQuery, Vertex AI, Dataflow, and data pipeline architecture — you can design a data platform, build a model, and troubleshoot a broken pipeline.
Working knowledge of agentic AI patterns — agent orchestration, tool use, grounding, retrieval-augmented generation.
Strong in both technical leadership and delivery management — architecture, scope, risk, stakeholders.
Experience with distributed global teams and modern engineering practices.
Clear communicator — whiteboarding with engineers or presenting to a C-suite.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)

Nice To Haves

Google Cloud Certifications: Active Google Cloud Professional certifications (specifically Professional Data Engineer, Professional Machine Learning Engineer, or Professional Cloud Architect).
Advanced Agent Deployments: Hands-on experience with the Gemini Enterprise Agent Platform, ADK, A2A, or MCP.
Production AI Experience: Experience building production-grade ML/AI systems — not just prototypes.
Cross-Functional Ecosystem Exposure: Secondary familiarity with adjacent Google domains to maximize mid-market account impact, including: Marketing & Personalization: Customer 360, CDP setups, and predictive segmentation using BigQuery + Vertex AI. Customer Engagement: GECX and CCAI platforms for AI-powered customer interactions built directly on the data layer. Infrastructure Foundations: GKE, Terraform, and progressive CI/CD data pipeline deployments. Cybersecurity: Google Security Operations and Agentic Defense architectures for securing data assets. Workspace Productive Tech: Gemini Enterprise for Workspace internal collaboration workflows.
Transformation Track Record: A documented track record delivering data platform modernizations and AI solutions at pace across multiple concurrent clients.
Data Governance Depth: Deep expertise in multi-pillar data governance, enterprise migration strategy, and lakehouse architecture on GCP.
Bachelor's in CS, Engineering, or related field — or 12 years equivalent experience.
Google Cloud Professional certifications (Data Engineer, ML Engineer, Cloud Architect)
Hands-on experience with Gemini Enterprise Agent Platform, ADK, A2A, or MCP
Experience building production ML/AI systems — not just prototypes
Track record delivering data platform modernizations and AI solutions at pace across multiple clients
Deep expertise in data governance, migration strategy, and lakehouse architecture on GCP

Responsibilities

Own the delivery. End-to-end. Scope, timeline, team structure, quality, outcome. You're the single point of accountability the client relies on.
Design how it gets delivered. Shape the delivery approach for complex data and AI engagements — define the delivery plan, phase the work, structure the team, and manage dependencies across data engineering, analytics, and AI workstreams. Partner with solution engineers who own the technical architecture; you own how it gets executed and shipped.
Provide technical advisory across the engagement — Guide our mid-market clients and teams on our offerings, technical implementation, and the underlying Google Cloud services, with a deep understanding of each client's existing environment and architecture.
Be the client's trusted delivery partner. You're the face of the engagement. Lead status reviews, manage expectations, navigate trade-offs in real time, and ensure the client always knows where things stand — especially when the work involves iterative AI development where scope evolves.
Get solutions into clients' hands fast. Pick up where the deal ends and make it real. Stand up the delivery, mobilize the team, and drive execution from day one — delivering proven capabilities in weeks, not quarters.
Lead across borders. Coordinate onshore delivery leadership with offshore engineering teams. Set priorities, define workstreams, remove blockers, and keep the entire delivery machine moving at pace.
Drive quality and outcomes. Establish delivery governance, define checkpoints, track progress against commitments, and ensure every engagement delivers measurable client value — from data platform readiness to AI models in production.