Google Data Specialist

Accenture•Albany, NY

About The Position

As a Google Data Specialist at Accenture, you will help deliver data modernization, analytics, and AI solutions on Google Cloud Platform (GCP). You will support architecture design, build data pipelines and models, perform analysis, and contribute to technical implementations under the guidance of senior team members. The role combines hands-on technical delivery with client collaboration and ongoing development of expertise in GCP Data & AI services and practices. You will help build data pipelines, ETL/ELT processes, and integrations; support machine-learning and agentic AI solution development; and contribute to data governance, quality, security, and operational processes. You will participate in client workshops, translate requirements into technical tasks, communicate progress and blockers, and collaborate with senior engineers and architects. Travel may be required, varying from 0% to 100% depending on business needs and client requirements.

Requirements

At least 2 years of hands-on experience in Data Engineering, Data Analytics, ML Engineering, or a related field.
At least 2 years of practical experience with Google Cloud Platform.
At least 2 years of experience with SQL, data modeling, and building data pipelines.
At least 2 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; alternatively, an Associate’s degree with a minimum of 6 years of work experience.
Applicants for employment in the United States must have work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

Familiarity 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 data ingestion patterns for batch and streaming data sources.
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, including monitoring, retraining, and deployment.
Support prompt engineering, embeddings, and retrieval-augmented generation (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 best practices for security, identity management, and compliance.
Support data validation, testing, and monitoring 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; travel may range from 0% to 100%.