Google Senior Data Engineer

Accenture•Albany, NY

About The Position

Accenture is hiring a Senior Data Engineer to help deliver data modernization, analytics, and AI solutions on Google Cloud Platform (GCP). The role supports architecture design and end-to-end technical delivery, including building data pipelines and models, performing analysis, and implementing solutions with guidance from senior team members. The engineer works directly with clients and collaborates with senior data engineers, ML engineers, and architects. The position contributes to data engineering and analytics work using services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Looker, and Looker Studio. It also supports development of machine-learning and AI solutions using Vertex AI, Gemini models and services, model APIs, embeddings, and retrieval-augmented generation (RAG), as well as MLOps processes. Additional work includes gathering requirements, supporting data governance, quality, security, and monitoring, and contributing to internal accelerators, documentation, and reusable components. Travel may range 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/GenAI tools; Vertex AI is preferred.
Bachelor’s degree or equivalent (minimum 12 years) work experience. Applicants with an associate’s 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 GCP 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 ML 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 RAG 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.
Support 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 data validation, testing, and monitoring operational processes.
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.
Travel as required by business needs and client requirements, which may range from 0% to 100%.