[Hiring] Google Data & AI Engineer - Specialist (Lead) @Accenture

Role Description

L9 Specialist who leads client-facing delivery of data, analytics, and AI solutions on Google Cloud, with deep GCP product expertise and domain depth in one or more of Data Platform Modernization, Analytics & BI, Generative & Agentic AI, or Customer 360. Drives end-to-end engineering (build-to-run), mentors teams, and shapes repeatable offerings (POVs, accelerators, reference architectures) tied to measurable outcomes.

Qualifications

• Hands-on Google Cloud (GCP) Data & AI product delivery (design/build/run in production) with deep expertise in the Google data and AI product portfolio.
• Strong programming experience in Python and SQL; Java/Scala with Apache Beam is an advantage.
• GCP data engineering depth: BigQuery (performance and cost optimization), Dataflow, Pub/Sub, Dataproc, Cloud Composer/Dataform, Cloud Storage/BigLake.
• Applied ML and Generative AI on GCP: Vertex AI (Pipelines, Model Registry, Endpoints), BigQuery ML, Gemini models, RAG, and Vector Search.
• Engineering fundamentals: Infrastructure as Code (Terraform), CI/CD, DataOps/MLOps, observability, and security basics (IAM, CMEK, DLP, Dataplex).
• Domain depth in one or more: Customer 360 & Marketing Intelligence, Data Platform Modernization, Analytics & BI (Looker), Generative & Agentic AI.
• Offering mindset: ability to contribute to reusable assets, POVs, accelerators, and estimates.

Requirements

• Google Cloud Professional Data Engineer or Professional Machine Learning Engineer certification (good to have).
• Experience with Looker (LookML), Dataplex governance, and agent frameworks (Vertex AI Agent Builder/Agent Development Kit).
• Experience mentoring engineers in an on-shore/off-shore Agile delivery model.

Roles & Responsibilities

• Hands-On Data & AI Engineering: Design, build, test, and deploy complex data pipelines, analytics, and ML/GenAI solutions on Google Cloud, working across the entire delivery lifecycle from requirements gathering to production support.
• Client-Facing Solutioning: Work directly with clients to understand business and data challenges, translate requirements into technical designs, and act as a technical advisor for the client throughout the project.
• Team Leadership & Collaboration: Lead and mentor on-shore and off-shore engineers, conduct design and code reviews, and collaborate with cross-functional stakeholders to ensure alignment and delivery excellence.
• Modern Engineering & Agile Delivery: Apply DataOps/MLOps practices and Infrastructure as Code (Terraform) within an Agile framework to deliver high-quality solutions at pace.
• Reusable Assets & Offerings: Build accelerators, reference architectures, and POVs that make Google Data & AI delivery repeatable and tied to measurable outcomes.

Leverage your expertise to deliver outstanding results in one or more of the following Google Cloud Data & AI domains:

• Data Platform & Modernization: Build and migrate data warehouses and lakehouses to BigQuery using Dataflow, Dataproc, Datastream, and Cloud Composer.
• Analytics & Business Intelligence: Deliver governed analytics using Looker (LookML) and BigQuery.
• Generative & Agentic AI: Contribute to the design and development of AI solutions using Vertex AI, Gemini models, and Retrieval-Augmented Generation (RAG).
• Applied ML & MLOps: Build, deploy, and monitor ML models using Vertex AI Pipelines, Model Registry, and BigQuery ML.
• Customer 360 & Marketing Intelligence: Unify customer data in GCP; use BigQuery and Vertex AI for predictive segments and personalization.
• Real-Time & Streaming Analytics: Build event-driven pipelines with Pub/Sub, Dataflow, and BigQuery.
• Data Governance & Security: Implement cataloging, lineage, quality, and access controls with Dataplex, DLP, and IAM.