Fetcherr is an AI-driven company specializing in deep learning, algorithmic trading, and large-scale data solutions. Our core technology, the Large Market Model (LMM), enables accurate demand forecasting and real-time, data-driven decision-making. Originally focused on the airline industry, Fetcherr is expanding its AI solutions across additional industries.
Fetcherr is looking for a highly skilled Senior Python Engineer to join the Platform team. You will build our "Data Platform as a Service" on Google Cloud Platform (GCP), creating the infrastructure that enables data scientists and engineers across the organization to develop, deploy, and manage data applications at scale.
In this role, you will design and implement scalable systems that power our data ecosystem, working with modern technologies and solving challenging distributed systems problems.
Why join this team?
- Build the foundation that powers pricing decisions for major airlines and expanding industries
- Work with a modern, cloud-native stack on GCP: Apache Iceberg, Spark, ClickHouse, Dagster, and Ray.
- Shape the architecture of a platform serving multiple business verticals
- Collaborate with a team of experienced engineers tackling hard data engineering challenges
Responsibilities:
- Internal Developer Platform (IDP): Build self-service tools and APIs that abstract complex infrastructure, prioritizing Developer Experience (DevEx) to accelerate data science workflows.
- Orchestration Framework: Develop and manage asset-based orchestration pipelines using Dagster. Create reusable components, libraries, and patterns to standardize data workflows across the organization.
- Data Architecture: Implement and optimize our Medallion architecture using Apache Iceberg, Spark, and ClickHouse.
- Cloud Infrastructure: Leverage GCP to build and manage infrastructure, including GKE, Cloud Storage, BigQuery, and networking resources.
- Engineering Excellence: Establish and maintain best practices for Python development, including coding standards, testing (unit, integration, E2E), environment reproducibility (e.g. dependency locking), and version control.
- Production Engineering: Manage production life cycles, from CI/CD to observability and incident response.