Google Senior Data Engineer

GCP Senior Data Engineer At Accenture in Scottsdale, Arizona, United States

Accenture is a premier Google Cloud partner helping organizations modernize data ecosystems, build real-time analytics capabilities, and responsibly scale AI. As part of Accenture Cloud First and the Accenture Google Business Group (AGBG), we deliver solutions leveraging Google Cloud's Data & AI platform-including BigQuery, Looker, Vertex AI, Gemini Foundation Models, and Gemini Enterprise.

A hands-on Engineer with foundational experience in Data Engineering, Analytics, or Machine Learning-now building deep expertise in Google Cloud Platform (GCP). You are eager to apply technical skills, learn advanced Data & AI patterns, and support delivery teams in designing and implementing modern data and AI solutions.

You're comfortable working directly with clients, supporting senior architects, and contributing to end-to-end project execution.

The Work (What You Will Do)

As a GCP Senior Data Engineer, you will help deliver data modernization, analytics, and AI solutions on GCP. You will support architecture design, build data pipelines and models, perform analysis, and contribute to technical implementations under guidance from senior team members.

1. Hands-On Technical Delivery

Build data pipelines, ETL/ELT processes, and integrations using GCP services such as: BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage

Assist with data modeling, performance tuning, and query optimization in BigQuery.

Implement data ingestion patterns for batch and streaming data sources.

Support development of dashboards and analytics products using Looker or Looker Studio.

2. Support Agentic AI & ML Solution Development

Assist in developing ML models and AI solutions using: Vertex AI, Gemini Foundation Models, Gemini Enterprise, Model APIs & Embeddings

Implement ML pipelines and help establish MLOps processes (monitoring, retraining, deployment).

Support prompt engineering, embeddings, and retrieval-augmented generation (RAG) experimentation.

Contribute to model testing, validation, and documentation.

3. Requirements Gathering & Client Collaboration

Participate in client workshops to understand data needs, use cases, and technical requirements.

Help translate functional requirements into technical tasks and implementation plans.

Communicate progress, blockers, and insights to project leads and client stakeholders.

4. Data Governance, Quality & Security Support

Implement metadata management, data quality checks, and lineage tracking using GCP tools (Dataplex, IAM).

Follow best practices for security, identity management, and compliance.

Support operational processes for data validation, testing, and monitoring.

5. Continuous Learning & Team Support

Learn and apply GCP Data & AI best practices across architectural patterns, engineering standards, and AI frameworks.

Collaborate closely with senior data engineers, ML engineers, and architects.

Contribute to internal accelerators, documentation, and reusable components.

Stay current with GCP releases, Gemini model updates, and modern engineering practices.

Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.

Here's what you need

Minimum of 5 years of hands-on experience in Data Engineering, Data Analytics, ML Engineering, or related fields.

Minimum of 4 years of practical experience with Google Cloud Platform.

Minimum of 5 years of experience with SQL, data modeling, and building data pipelines.

Minimum of 3 years of experience with Python or AI or GenAI tools (Vertex AI preferred).

Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience)

Bonus point if you have

Experience with GCP services such as BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage, and Looker.

Exposure to AI/ML development or experimentation with Vertex AI, Gemini models, embeddings, or RAG patterns.

Hands-on experience with CI/CD, Git, or cloud-native engineering practices.

Google Cloud certifications (Associate Cloud Engineer or Professional Data Engineer).

Experience working in agile delivery environments.