Google Senior Data Engineer
About The Position
Accenture is a Google Cloud partner helping organizations modernize data ecosystems, build real-time analytics capabilities, and scale AI responsibly. As part of Accenture Cloud First and the Accenture Google Business Group, this role delivers solutions using Google Cloud’s Data & AI platform, including BigQuery, Looker, Vertex AI, Gemini Foundation Models, and Gemini Enterprise. The GCP Senior Data Engineer helps deliver data modernization, analytics, and AI solutions on Google Cloud. The role supports architecture design and technical implementation, builds data pipelines and models, performs analysis, and contributes to end-to-end project execution under the guidance of senior team members. The engineer works directly with clients and collaborates with senior data engineers, ML engineers, and architects. The work includes developing batch and streaming data ingestion and ETL/ELT processes; supporting BigQuery data modeling, performance tuning, and query optimization; and assisting with dashboards and analytics products. The engineer also contributes to machine learning and AI solution development, MLOps, prompt engineering, embeddings, and retrieval-augmented generation experimentation. Additional responsibilities include gathering client requirements, supporting data governance and security, and contributing to team documentation and reusable components. Travel may be required, ranging from 0 to 100% depending on business needs and client requirements.
Requirements
- At least 5 years of hands-on experience in Data Engineering, Data Analytics, ML Engineering, or a related field.
- At least 4 years of practical experience with Google Cloud Platform.
- At least 5 years of experience with SQL, data modeling, and building data pipelines.
- At least 3 years of experience with Python or AI or GenAI tools; Vertex AI is preferred.
- Bachelor’s degree or equivalent, with a minimum of 12 years of work experience. Applicants with an associate degree must have at least 6 years of work experience.
- Applicants for US employment must have work authorization that does not now or in the future require visa sponsorship.
Nice To Haves
- 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, such as Associate Cloud Engineer or Professional Data Engineer.
- Experience working in agile delivery environments.
Responsibilities
- Build data pipelines, ETL/ELT processes, and integrations using Google Cloud services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.
- Assist with data modeling, performance tuning, and query optimization in BigQuery.
- Implement batch and streaming data ingestion patterns.
- Support development of dashboards and analytics products using Looker or Looker Studio.
- Assist in developing machine learning models and AI solutions using Vertex AI, Gemini Foundation Models, Gemini Enterprise, model APIs, and embeddings.
- Implement ML pipelines and help establish MLOps processes for monitoring, retraining, and deployment.
- Support prompt engineering, embeddings, and retrieval-augmented generation experimentation.
- Contribute to model testing, validation, and documentation.
- 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.
- Implement metadata management, data quality checks, and lineage tracking using GCP tools such as Dataplex and IAM.
- Follow security, identity management, and compliance best practices.
- Support operational processes for data validation, testing, and monitoring.
- Learn and apply GCP Data & AI best practices, engineering standards, architectural patterns, and AI frameworks.
- Collaborate 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.
Benefits
- Medical coverage
- Dental coverage
- Vision coverage
- Life insurance
- Long-term disability coverage
- 401(k) plan
- Bonus opportunities
- Paid holidays
- Paid time off
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What This Job Offers
Job Type
Full-time
Career Level
Senior
