AGBG Google Data & AI Engineer - Senior Analyst

Job Title - AGBG Google Data & AI Engineer – Senior Analyst Management Level: 10-Senior Analyst Location: All over India Must have skills Hands-on experience building data or ML solutions on Google Cloud (GCP) that run in production. Solid programming skills in Python and SQL; exposure to Java/Scala or Apache Beam is an advantage. Working knowledge of core GCP data services: BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer/Dataform. Exposure to ML and Generative AI on GCP: Vertex AI (training, pipelines, endpoints), BigQuery ML, Gemini models, and basic RAG patterns. Engineering basics: Git, CI/CD, Terraform fundamentals, automated testing, and secure access (IAM, secrets). Familiarity with one or more domains: Data Platform Modernization, Analytics & BI (Looker), Generative AI, or Customer 360. Strong problem-solving, communication, and ability to work in an Agile team. Good to have Google Cloud Associate Cloud Engineer or Professional Data Engineer certification (or in progress). Exposure to Looker (LookML), Dataplex, or Vertex AI Agent Builder. Experience working with on-shore/off-shore teams. Job Summary As a Google Data & AI Engineer (Senior Analyst), you will build and operate data pipelines, analytics, and ML/GenAI components on Google Cloud as a core member of a delivery team. You will work from defined designs and guidance from leads, own well-scoped features end to end, and ensure quality across data accuracy, security, performance, and cost, while growing your expertise in Google's Data & AI portfolio. Roles & Responsibilities Hands-On Data & AI Development: Build, test, and deploy data pipelines, transformations, dashboards, and ML/GenAI components on Google Cloud, from requirements through production support. Client & Team Collaboration: Participate in client discussions, clarify requirements, and communicate progress and risks clearly to leads and stakeholders. Quality & Peer Reviews: Write clean, tested, documented code; participate in peer reviews; and monitor pipelines and models to resolve data quality and performance issues. Modern Engineering & Agile Delivery: Apply DataOps/MLOps practices, CI/CD, and Infrastructure as Code (Terraform) within an Agile framework, and estimate your own tasks. Learning & Contribution: Contribute to reusable assets and accelerators, support junior team members, and keep building Google Cloud skills and certifications. Contribute to delivery in one or more of the following Google Cloud Data & AI areas: – Data Platform & Modernization: Build and migrate data to BigQuery using Dataflow, Datastream, and Cloud Composer. – Analytics & Business Intelligence: Develop dashboards and semantic models using Looker and BigQuery. – Generative AI: Contribute to solutions using Vertex AI, Gemini models, and Retrieval-Augmented Generation (RAG). – Applied ML & MLOps: Train, deploy, and monitor models using Vertex AI Pipelines and BigQuery ML. – Customer 360 & Marketing Intelligence: Prepare and unify customer data in BigQuery to support segmentation and personalization. – Real-Time & Streaming Analytics: Build and support streaming pipelines with Pub/Sub, Dataflow, and BigQuery. About Our Company | Accenture