Project Role: Technology Architect

Project Role Description: Design and deliver technology architecture for a platform, product, or engagement. Define solutions to meet performance, capability, and scalability needs.

Must have skills: Google Cloud Platform Architecture

Good to have skills: Google Big Query

Minimum 7.5 year(s) of experience is required

Educational Qualification: 15 years full time education

Summary:

• As a Data Engineer
• AI/ML, you will be responsible for designing, building, and maintaining scalable data pipelines and systems that power AI/ML applications on Cloud platforms. Your typical day will involve leveraging Google Cloud’s data services, implementing GenAI and AI/ML models, and supporting data-driven solutions through efficient architecture and engineering.

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Roles & Responsibilities:

i. Design and develop scalable data pipelines and ETL processes using Google Cloud data services like Big Query, Dataflow, Pub/Sub, and Dataproc.
ii. Build and optimize data architectures to support AI/ML applications and model training at scale.
iii. Collaborate with data scientists and ML engineers to implement data ingestion, feature engineering, and model-serving pipelines.
iv. Develop and manage data integration solutions that align with enterprise data governance and security standards.
v. Support GenAI/Vertex AI model deployment by ensuring reliable data access and transformation pipelines.
vi. Implement monitoring, logging, and alerting for data workflows and ensure data quality across all stages.
vii. Enable self-service analytics by building reusable data assets and data marts for business stakeholders.
viii. Ensure cloud-native, production-grade data pipelines and participate in performance tuning and cost optimization.
ix. Experience with programming languages such as Python, SQL, and optionally Java or Scala.

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Professional & Technical Skills:

• Must To Have Skills: Strong experience in Google Cloud Data Services (Big Query, Dataflow, Pub/Sub) and hands-on with scalable data engineering pipelines.
• Good To Have Skills: GenAI/Vertex AI exposure, Cloud Data Architecture, PCA/PDE certifications.
• Understanding of data modeling, data warehousing, and distributed computing frameworks.
• Experience with AI/ML data pipelines, MLOps practices, and model deployment workflows.
• Familiarity with CI/CD and infrastructure-as-code tools (Terraform, Cloud Build, etc.) for data projects.

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Additional Information:

• The candidate should have a minimum of 9 years of experience in Google Cloud Data Engineering or related domains.
• The ideal candidate will possess a strong educational background in computer science, data engineering, or a related field, along with a proven track record of building and scaling data systems for AI/ML initiatives.

15 years full time education

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