- Proficient in BigQuery, Dataflow, Cloud Build, SQL, Terraform, Git, experience with data modeling (dimensional modeling, star schema), ETL processes, and GCP cost optimization; LookML experience is a plus.

BigQuery :

- Extensive experience designing, developing, and optimizing data warehouses and data marts within BigQuery, including deep knowledge of its architecture, partitioning, clustering, and performance tuning.

Cloud Build :

- Proficiency in building and maintaining complex ETL pipelines using these and other relevant GCP services for data extraction, transformation, and loading.

SQL :

- Advanced proficiency with a focus on query optimization and performance tuning within BigQuery.

Terraform :

- Proven experience managing GCP infrastructure as code, specifically BigQuery resources.

Git :

- Proficiency with Git and version control best practices.

Data Modeling :

- Strong skills in designing dimensional models and star schemas, translating business requirements into efficient data structures.

GCP Cost Optimization :

- Ability to estimate, track, and minimize GCP costs related to data processing and storage.

ETL :

- Experience designing, building, and maintaining complex ETL processes.

Collaboration and Communication :

- Excellent interpersonal and communication skills.

Preferred candidate profile :

- Design and own end-to-end AI product architecture leveraging GCPs AI and data services, ensuring scalability, performance, and cost efficiency.

- Collaborate closely with data scientists and MLOps teams to design scalable, production-ready ML code structures and efficient ML pipelines.

- Partner with data engineers, acting as a data architect, to ensure data models, pipelines, and storage systems are optimized for AI workloads and adhere to architectural principles.

- Support full stack developers by advising on infrastructure design, microservice architecture, and GCP implementation best practices.