Python Data Platform Engineer

Were a team in Developer Observability, focused on giving engineers the tools and insights they need to ship faster and smarter. Our work supports teams across the organization by improving decision-making and development workflows. We operate as a distributed team across the US and Poland.

• Bachelors degree plus 3 years of related experience, or Masters degree plus 2 years of related experience.
• Hands-on experience with Python, including OOP, error handling, REST API integrations, and clean code practices.
• Good understanding of SQL and data engineering fundamentals.
• Familiarity with AI tools and technologies to improve engineering workflows, automation, and productivity.
• Solid understanding of Git and modern software development workflows.
• Experience building or maintaining CI/CD pipelines, preferably using GitLab CI.
• Good engineering fundamentals including system design concepts, technical documentation, and architecture diagrams.
• Strong communication and collaboration skills, including participating in code reviews and stakeholder interaction.

Nice to Have:

• Working knowledge of AWS services such as IAM, S3, Glue, Athena, and MWAA/Airflow.
• Experience with cloud data platforms such as Snowflake or Redshift.
• Familiarity with observability and monitoring tools such as Prometheus, Splunk, or OpenTelemetry.
• Familiarity with Docker and Kubernetes.
• Experience with Terraform for infrastructure as code.
• Experience in developer productivity engineering, improving tooling and workflows throughout the SDLC.

• Contribute to end-to-end system design and participate in technical and architectural decisions.
• Build, maintain, and optimize Python-based services, engineering tools, and data pipelines.
• Work with data across the full lifecycle, including data extraction, transformation and delivery.
• Develop and maintain workflow orchestration using Apache Airflow, including pipeline testing, monitoring, and reliability.
• Work with Snowflake as a data platform and dbt.
• Build and maintain CI/CD pipelines, deployment processes, and environment automation.
• Containerize and deploy applications using Docker and Kubernetes.
• Actively adopt and contribute to AI-assisted engineering and testing practices.
• Follow and contribute to engineering standards for code quality, testing, documentation, and development best practices.
• Collaborate with engineers, data teams, and stakeholders to design and deliver reliable, scalable solutions.