[Hiring] Google Data & AI Engineer - Associate Manager @Accenture

Role Description

As a Google Data & AI Engineer (Associate Manager), you will deliver data platforms, analytics, and AI solutions on Google Cloud, owning end-to-end execution from data ingestion and modelling to ML/GenAI and consumption. You will shape solutions with clients and Google partners, lead a small team, and ensure production-ready quality, security, reliability, performance, and cost. You will stay hands-on in architecture and development while driving DataOps/MLOps, CI/CD, and IaC.

Qualifications

• Hands-on GCP Data & AI product delivery (design, build, run in production), not just custom development hosted on GCP.
• Strong Python and SQL skills; Java/Scala with Apache Beam is a plus.
• Deep GCP data engineering: BigQuery (partitioning, clustering, cost optimization), Dataflow, Pub/Sub, Dataproc, Cloud Composer/Dataform, Cloud Storage/BigLake, Datastream.
• Applied ML and GenAI on GCP: Vertex AI (Pipelines, Model Registry, Endpoints), BigQuery ML, Gemini, RAG, Vector Search, and agent frameworks (Agent Builder/ADK).
• Platform fundamentals: Terraform (IaC), CI/CD, DataOps/MLOps, observability, and security & governance (IAM, VPC Service Controls, CMEK, DLP, Dataplex).
• Domain depth in one or more of the Google Cloud Data & AI domains listed below.
• Strong offering mindset: solutioning, reusable assets, POVs, estimation, and value-based outcomes.

Requirements

• Google Cloud Professional Data Engineer, ML Engineer, or Cloud Architect certification (good to have).
• Experience with Looker (LookML), Dataplex governance, and Analytics Hub data sharing (good to have).
• Experience leading on-shore/off-shore Agile teams (good to have).

Roles & Responsibilities

• Hands-On Data & AI Engineering: Design, build, test, and deploy data pipelines, analytics, and ML/GenAI solutions on Google Cloud, from requirements to production support.
• Client-Facing Solutioning: Work with clients to understand business and data challenges, translate requirements into target-state data and AI architectures, and act as a trusted technical advisor.
• Team Leadership & Collaboration: Manage and mentor on-shore/off-shore teams, conduct design and code reviews, and collaborate with data owners, product, security, and business teams.
• Modern Engineering & Agile Delivery: Apply DataOps/MLOps, Terraform, automated testing, and CI/CD in an Agile model to deliver quality solutions at pace.
• Delivery Governance & FinOps: Own data quality, SLAs, lineage, access control, and cloud cost (BigQuery slots/reservations, storage tiers) of delivered solutions.
• Offerings & Growth: Contribute to POVs, reusable accelerators, reference architectures, estimates, and proposals that tie Google Data & AI capabilities to measurable business outcomes.

Benefits

• Deliver outstanding results in one or more of these Google Cloud Data & AI domains:
• Data Platform & Modernization: Build and migrate enterprise warehouses and lakehouses to BigQuery/BigLake using Dataflow, Dataproc, Datastream, and Cloud Composer.
• Analytics & Business Intelligence: Deliver governed self-service analytics using Looker (LookML), BigQuery BI Engine, and Looker Studio.
• Generative & Agentic AI: Design and develop AI solutions using Vertex AI, Gemini, RAG, Vector Search, and multi-agent workflows.
• Applied ML & MLOps: Build, deploy, and monitor models using Vertex AI Pipelines, Feature Store, Model Registry, and BigQuery ML with automated retraining.
• Customer 360 & Marketing Intelligence: Unify customer data in GCP; use BigQuery and Vertex AI for identity resolution, predictive segments, and personalization.
• Real-Time & Streaming Analytics: Build event-driven pipelines with Pub/Sub, Dataflow, and BigQuery streaming for near-real-time insights.
• Data Governance & Security: Implement cataloging, lineage, quality, and policy controls with Dataplex, DLP, IAM, and VPC Service Controls.