Marketing, MMM GCP
Accenture in India
This job at a glance
- Who can apply
- Freshers
- Must know
- GCPREST APIsMachine learningLLMsETLStakeholder management
- Work
- On-site in Bengaluru
- Pay
- Not stated by the employer.
- Skill demand
- REST APIs8%Machine learning8%LLMs8%GCP4%of 26 similar fresher jobs posted in India this week
- Listed
- 4 Nov 2026 · still open
Sign in free to see which of these skills your resume already shows.
About the Role
As a Data Engineering Consultant specializing in GCP Architecture, you will be responsible for designing, building, and optimizing cloud-native data solutions on Google Cloud Platform. Your typical day will involve architecting scalable data pipelines using BigQuery, Dataflow, and Pub/Sub, integrating generative AI capabilities via the Gemini API, and deploying ML workflows through Vertex AI Pipelines. You will work closely with data architects and engineering teams to translate business requirements into production-grade GCP data architectures and deliver robust GCP solutions.
Key Responsibilities
- Design and implement end-to-end data architectures on GCP leveraging BigQuery, Dataflow, Pub/Sub, Vertex AI Pipelines, and Gemini API.
- Build scalable, serverless streaming and batch data pipelines using Apache Beam and Cloud Dataflow.
- Integrate Gemini API for generative AI use cases within data products and analytical workflows.
- Orchestrate ML training and inference pipelines on Vertex AI, ensuring reliability and performance at scale.
- Configure real-time event-driven architectures using Pub/Sub for data ingestion and messaging.
- Collaborate with data scientists, analysts, and client stakeholders to define data platform requirements.
- Conduct performance tuning, cost optimization, and governance reviews for GCP environments.
- Stay current with GCP product releases and AI/ML advancements, recommending adoption where impactful.
Requirements & Tech Stack
- Google Cloud Platform (GCP)
- BigQuery
- Dataflow
- Pub/Sub
- Vertex AI Pipelines
- Gemini API
- Apache Beam
- Machine Learning (ML)
- Artificial Intelligence (AI)