Google Data & AI Engineer Lead - Specialist

Job Title - AGBG Google Data & AI Engineer – Specialist (Lead) Management Level: 09-Specialist Location: All over India Must have skills 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. Good to have Google Cloud Professional Data Engineer or Professional Machine Learning Engineer certification. 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. Job Summary 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. 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. About Our Company | Accenture

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