Google Cloud - Software Developer - Python - Big Data :: Remote

Role : Software Developer

Locations: Remote

Rate: On C2C

All Visa

Responsibilities :

• Semantic Modeling Research: Research and compare the semantic and metric modeling capabilities of Looker, dbt (MetricFlow), Snowflake, and Databricks. Deliver a prioritized backlog of BQ measure features with a repository of real-world samples and direct source links.
• dbt Package Development: Build, publish, and maintain an open-source dbt package library to introduce BigQuery graph as a dbt materialization.
• CI/CD & Automation: Develop reference CI/CD pipelines (e.g., GitHub Actions, GitLab CI) that parse, validate, and deploy BigQuery graphs defined in declarative YAML formats.
• Developer Enablement: Create high-quality developer documentation, reference implementations, and tutorials to drive community adoption of BigQuery Graphs and Measure

• Qualifications :
• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience. At least 4 years of software development experience, with a strong proficiency in Python packaging, testing (pytest), and validation.
• Extensive hands-on experience with dbt (dbt-core), specifically in building custom materializations, Jinja macros, and package integrations.
• Strong SQL proficiency (including complex aggregations, window functions, and schema modeling) and deep production experience with Google Cloud BigQuery.
• Analytical mindset with the ability to research complex technical specifications and translate findings into structured backlogs and architectural designs

• Desired Skills:
• Hands-on experience modeling with metric/semantic layers (e.g., LookML, dbt MetricFlow, Cube, or Snowflake/Databricks metric features).
• Experience with Property Graphs queries ISO GQL, SQL or graph database engines.
• Experience with CI/CD tools and declarative schema compilers (e.g., YAML/JSON validation via Pydantic).

Active participation or maintainer status in open-source data communities.