Tech Stack
AirflowAWSBigQueryCloudDockerETLGoogle Cloud PlatformKubernetesLinuxPythonSQLTerraform
About the role
- Understand, format and prepare data for analytics and data-science processes.
- Design, build, and optimize scalable ETL/ELT pipelines for batch and streaming data.
- Collaborate with analysts to understand data needs and ensure accessible, well-modeled data sets.
- Dive deep into system metrics and usage patterns to identify opportunities for FinOps-driven cost savings.
- Manage data infrastructure on GCP (BigQuery, Cloud Composer,Vertex AI, Kubernetes, etc.).
- Automate infrastructure provisioning using Pulumi or Terraform.
- Set up data quality monitoring, alerting, and logging systems.
- Collaborate with data scientists and ML engineers to productionize models and build supporting pipelines.
- Continuously improve performance, scalability, and cost-efficiency of data workflows.
Requirements
- Strong experience with Python and SQL for data engineering.
- Solid understanding of cloud platforms (ideally GCP) and data services (BigQuery, Cloud Storage, etc.).
- Hands-on experience with Infrastructure-as-Code tools like Pulumi or Terraform.
- Experience with Airflow, dbt, or similar orchestration/transform tools.
- Proficiency in Docker and Kubernetes for data workflows.
- Understanding of Linux systems, cloud networking, and security best practices.
- Experience with CI/CD pipelines and version control (GitLab or similar).
- A mindset for continuous improvement, optimization, and working cross-functionally.
- Previous exposure to FinOps practices or cost-optimization work in cloud environments. (plus)
- Experience with ClickHouse. (plus)
- Experience with AWS. (plus)
- Familiarity with iGaming, B2B SaaS, or Fintech domains. (plus)
- Experience supporting data science/ML workflows in production. (plus)
- Cloud/data-related certifications. (plus)