Join our delivery team as a Google Data & AI Engineer (Senior Analyst) to build and manage data pipelines, analytics, and ML/GenAI solutions on Google Cloud. You will play a key role in designing and implementing features, ensuring high quality in data accuracy, security, performance, and cost-efficiency. This role offers a fantastic opportunity to deepen your expertise within Google's comprehensive Data & AI ecosystem.

This position involves contributing to various Google Cloud Data & AI domains, including Data Platform Modernization, Analytics & Business Intelligence, Generative AI, Applied ML & MLOps, Customer 360, and Real-Time Streaming Analytics. You will work with defined designs and guidance, taking ownership of well-scoped features from inception to production support.

Develop, test, and deploy data pipelines, transformations, dashboards, and ML/GenAI components on Google Cloud, managing the entire lifecycle from requirements gathering to production support. Engage in client discussions to clarify requirements and effectively communicate progress and potential risks to leads and stakeholders.

Ensure code quality through writing clean, tested, and documented code, actively participating in peer reviews. Monitor pipelines and models to proactively address data quality and performance issues. Embrace modern engineering practices such as DataOps/MLOps, CI/CD, and Infrastructure as Code (Terraform) within an Agile framework, while also providing accurate task estimations.

A minimum of 2-5 years of professional experience is required for this role. Candidates must possess hands-on experience building production-ready data or ML solutions on Google Cloud Platform (GCP). Strong programming skills in Python and SQL are essential, with an advantage given to those with exposure to Java/Scala or Apache Beam.

Proficiency with core GCP data services including BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer/Dataform is necessary. Familiarity with ML and Generative AI on GCP, such as Vertex AI, BigQuery ML, Gemini models, and basic RAG patterns, is expected. Fundamental engineering skills like Git, CI/CD, Terraform, automated testing, and secure access (IAM) are also required.