Embedded Python Data & Automation Engineer – Data Pipelines, APIs & Automation | Remote
Position Type: Full-Time, Remote
Working Hours: Meaningful Overlap with U.S. Business Hours
About the Role
At Pavago, one of our clients is hiring an experienced Embedded Python Data & Automation Engineer to take ownership of an existing Python environment, maintain production systems, improve data pipelines and automations, and develop practical internal tools and integrations.
This is a hands-on engineering role with an important initial focus on system transition and knowledge transfer. You’ll work closely with a departing programmer to understand the existing Python codebase, production workflows, integrations, dependencies, and automations before assuming ownership of the environment.
As the transition progresses, your focus will shift toward improving reliability, reducing technical debt, expanding automations, strengthening integrations, and building new internal tools based on business needs.
If you’re comfortable inheriting an existing codebase, troubleshooting production systems, and gradually making them more reliable and maintainable, this role is a strong fit.
What You’ll Own
System Transition & Technical Ownership
- Shadow the departing programmer and absorb critical system knowledge
- Take ownership of the existing Python codebase
- Understand existing:
- Applications
- Data pipelines
- Scheduled automations
- APIs and integrations
- Dependencies
- Deployment workflows
- Identify undocumented processes and system dependencies
- Document critical knowledge throughout the handover process
- Develop sufficient technical context to independently maintain and extend the environment
- Ensure a smooth transition with minimal disruption to production systems
Production Support & Maintenance
- Troubleshoot live production issues
- Maintain existing applications, scripts, and automated processes
- Investigate failures and unexpected system behavior
- Identify root causes and implement reliable fixes
- Monitor system stability and recurring technical issues
- Raise technical risks early
- Prioritize reliability when modifying existing production systems
Data Pipelines & Automation
- Maintain, debug, and improve production data pipelines
- Monitor scheduled automations and investigate failures
- Improve pipeline reliability and maintainability
- Build new automations based on business requirements
- Reduce repetitive manual processes through practical engineering solutions
- Identify opportunities to improve existing automated workflows
- Ensure critical jobs and pipelines continue operating reliably
APIs & Integrations
- Maintain and extend existing APIs and third-party integrations
- Work with:
- APIs
- Authentication flows
- Webhooks
- Third-party services
- Troubleshoot integration and authentication issues
- Maintain reliable data exchange between systems
- Extend integrations as business requirements evolve
- Document integration logic and dependencies
Technical Debt & System Reliability
- Identify:
- Fragile systems
- Undocumented dependencies
- Technical risks
- Maintenance bottlenecks
- Prioritize technical debt based on operational impact
- Refactor and improve existing systems over time
- Reduce unnecessary complexity where appropriate
- Strengthen system reliability without disrupting production
- Flag areas where existing architecture could create future operational risk
Documentation & Knowledge Management
- Create and maintain:
- Technical documentation
- Workflow diagrams
- Dependency maps
- Operating procedures
- Document systems as you learn and modify them
- Keep technical documentation current as workflows evolve
- Ensure critical system knowledge is not dependent on a single individual
- Make troubleshooting and future development easier through clear documentation
Internal Tooling & Development
- Build practical automation tools and internal software based on business needs
- Translate operational requirements into technical solutions
- Scope new internal tools with client stakeholders
- Extend existing systems where appropriate
- Balance new development with production support and maintenance
- Build solutions that improve operational efficiency and reduce manual work
Engineering Practices
- Use Git and pull requests for version control and code review
- Write and maintain automated tests where appropriate
- Maintain clear changelogs
- Use staged deployment workflows
- Test changes before releasing them into production
- Follow disciplined engineering practices while working within an existing environment
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
- Third-party integrations
- Authentication
- Webhooks
- Experience working with an existing or legacy codebase
- Working knowledge of SQL and relational databases
- Familiarity with:
- Git
- Pull requests
- Automated testing
- Deployment workflows
- Strong troubleshooting and analytical skills
- Strong written English communication
- Strong technical documentation skills
- Ability to independently understand unfamiliar systems and code
- Ability to work with meaningful overlap with U.S. business hours
Nice to Have
- Experience taking ownership of systems previously maintained by another engineer
- Experience improving or modernizing legacy Python environments
- Experience developing internal business tools
- Experience with scheduled jobs and automation workflows
- Strong understanding of data pipeline reliability
- Experience with staged production deployments
- Experience working directly with business stakeholders to scope technical solutions
Tools & Technology
Python | SQL | Relational Databases | APIs | Webhooks | Authentication | Git | Pull Requests | Automated Testing | Data Pipelines | Automation | Staged Deployments | Slack
What Makes You a Strong Fit
You’ll likely thrive in this role if you:
- Are comfortable inheriting and understanding someone else’s code
- Can navigate an unfamiliar production environment methodically
- Troubleshoot technical problems rather than simply patching symptoms
- Enjoy building automations that eliminate repetitive work
- Understand how data pipelines, APIs, databases, and integrations work together
- Document systems as you work
- Identify fragile systems and technical risks before they become major problems
- Communicate technical issues clearly and raise concerns early
- Can balance production maintenance with new development
- Prefer testing and staged releases over making unverified production changes
- Take ownership of systems from problem identification through resolution
What a Typical Day Looks Like
Your day may begin by checking overnight automations and reviewing any open production issues.
You’ll spend focused blocks working through the existing environment — reading Python code, running tests, tracing integrations, troubleshooting data pipelines, and filling documentation gaps.
Early in the engagement, a meaningful portion of your time will be spent in handover sessions with the departing programmer. As the transition matures, that time will increasingly shift toward new development, including building automations, improving pipeline reliability, and scoping internal tools with the client.
You’ll communicate primarily through Slack and participate in planning sessions and check-ins that align with U.S. business hours. You’ll be expected to raise flags early, document as you go, and push changes through staging rather than directly to production.
In short: you take ownership of an existing Python environment, keep critical systems running, and progressively improve the pipelines, automations, integrations, and internal tools the business depends on.
Key Metrics for Success
- Smooth knowledge transfer from the existing programmer
- Stable and reliable production systems
- Successful execution of scheduled automations
- Reduced recurring pipeline and integration failures
- Faster identification and resolution of production issues
- Improved technical documentation coverage
- Reduction in fragile or undocumented dependencies
- Reliable APIs and third-party integrations
- Consistent use of testing and staged deployment practices
- Successful delivery of new automations and internal tools
Why This Role Stands Out
- Direct ownership of an established production Python environment
- Hands-on work across Python, SQL, data pipelines, APIs, integrations, and automation
- Meaningful responsibility from the beginning through a structured technical handover
- Balance between production engineering and new development
- Opportunity to reduce technical debt and improve engineering practices
- Direct collaboration with client stakeholders
- Fully remote working environment
- Career growth opportunities into:
- Senior Python Engineer
- Data Engineer
- Automation Engineer
- Technical Lead
- Data & Automation Engineering Leadership
Interview Process
- Initial Application
- Spark Hire One-Way Video Interview
- Video Interview Screening
- Client Interview
- Offer Stage
Spark Hire Video Interview – Required
As part of the application process, all candidates are required to complete a one-way video interview through Spark Hire.
After completing the first step of your application, you’ll receive a Spark Hire invitation by email with instructions to record and submit your video responses.
Completion of the Spark Hire video is required to be considered for the next stage. Please check your inbox as well as your spam or junk folder for the invitation.
Apply Now
If you’re an experienced Python Engineer, Data Engineer, or Automation Engineer with hands-on experience maintaining production pipelines, APIs, integrations, and existing codebases, we’d love to hear from you.
Apply today and take ownership of the Python systems, data pipelines, automations, and integrations that support critical business operations.
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