Own product engineering across backend services, AI integrations, and user-facing features for complex document workflows, from technical design and release through iteration, reliability, and production quality.
Embedded Python Data & Automation Engineer
π Description
- Take ownership of the existing Python codebase through handover and knowledge transfer.
- Troubleshoot production issues and maintain system stability.
- Maintain, debug, and improve data pipelines and scheduled automations.
- Own and extend APIs, integrations, authentication flows, and webhooks.
- Identify technical debt and document systems, workflows, dependencies, and operating procedures.
- Build internal automation tools and software using Git, testing, pull requests, and staged deployments.
π οΈ Requirements
- 3+ years of professional Python experience in a data-focused environment.
- Experience building or maintaining production data pipelines and automations.
- Strong experience with APIs, integrations, authentication, and webhooks.
- Experience working with an existing or legacy codebase.
- Working knowledge of SQL and relational databases.
- Familiarity with Git, pull requests, testing, and deployment workflows.
- Strong written English and technical documentation skills.
Full job description
About the Role
We are looking for an Embedded Python Data & Automation Engineer to take ownership of an existing Python environment, maintain production systems, improve automations and data pipelines, and develop internal tools and integrations.
Key Responsibilities
- System Transition & Knowledge Absorption: Shadow the departing programmer and take ownership of the existing Python codebase.
- Production Support & Maintenance: Troubleshoot live issues, maintain existing systems, and ensure system stability.
- Data Pipelines & Automations: Maintain, debug, and improve data pipelines and scheduled automations.
- API & Integration Ownership: Maintain and extend APIs, third-party integrations, authentication flows, and webhooks.
- Technical Debt: Identify fragile systems, undocumented dependencies, and technical risks and improve them over time.
- Documentation: Create and maintain system documentation, workflow diagrams, dependency maps, and operating procedures.
- Internal Tooling: Build practical automation tools and internal software based on business needs.
- Engineering Practices: Use Git, pull requests, automated testing, changelogs, and staged deployments.
Required Experience & Skills
- 3+ years of professional Python experience in a data-focused environment.
- Experience building or maintaining production data pipelines and automations.
- Strong experience with APIs, integrations, authentication, and webhooks.
- Experience working with an existing or legacy codebase.
- Working knowledge of SQL and relational databases.
- Familiarity with Git, pull requests, testing, and deployment workflows.
- Strong written English and technical documentation skills.
- Ability to work with meaningful overlap with US business hours.
Typical Day
Your day begins with a check on overnight automations and any open production issues. You spend focused blocks working through the current system β reading existing code, running tests, tracing integrations, and filling in documentation gaps. Early in the engagement, a meaningful portion of your time is in handover sessions with the departing programmer. As the transition matures, that time shifts toward new development: building automations, improving pipeline reliability, or scoping a new internal tool with the client. You communicate primarily over Slack and participate in planning sessions and check-ins that align with US business hours. You raise flags early, document as you go, and push changes through staging rather than directly to production.
Interview Process
- Video Interview Screening
- Client Interview
- Offer Stage
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