• Build robust solutions for managing real-time and batch data, including data integrations with partners via feeds or API, and interactive data reporting for client-facing applications.
• Develop hardened and repeatable CI/CD data models and pipelines to enable reporting, modeling, and machine learning.
• Design and implement data pipelines to support blending data from multiple sources and machine learning models, ensuring efficient data processing and feature engineering.
• Improve data availability and quality for our enterprise clients through automated monitoring and alerting.
• Leverage Google Cloud (GCP) tools and other services (e.g., Astronomer - Apache Airflow) to bring data workloads to production.
• Enable end-user configuration of product features, ensuring seamless synchronization with the broader application.
• Collaborate with cross-functional teams to deliver informed solutions that meet platform and client needs.
• Make team-based decisions, fostering shared responsibility for defensible design considerations.

YOUR EXPERIENCE

• 5+ years working in a data engineering role supporting product, analytics and data science teams
• Proficient in SQL, and schema design, with experience in columnar databases such as Google BigQuery, Snowflake, or Amazon Redshift (familiarity with GraphQL a plus)
• 4+ years of Python (or similar experience) writing efficient, testable, and readable code
• Experience building and optimizing real-time and batch processing solutions, ensuring high availability and low latency, allowing for timely insights and actions.
• Skilled in designing end-to-end data pipelines in cloud frameworks (GCP, AWS, Azure) with multi-stakeholder requirements
• Familiarity with Google Cloud (GCP) tools (e.g., Cloud Run, Cloud Functions, Vertex AI, App Engine, Cloud Storage, IAM)
• Experience with CI/CD pipelines for data processing (Docker, CircleCI, dbt, git)
• Proficient in Infrastructure as Code (Terraform or Pulumi) and data orchestration tools (e.g., Apache Airflow