Embedded Python Data & Automation Engineer

Embedded Python Data & Automation Engineer – APIs, Data Pipelines & Internal Tools | 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 build practical internal tools and integrations.

This is a hands-on engineering role with an important initial focus on knowledge transfer and system ownership. You’ll work closely with a departing programmer to understand the existing codebase, integrations, dependencies, automations, and production workflows before becoming the primary technical owner.

Beyond maintaining what already exists, you’ll identify technical risks, improve reliability, reduce technical debt, strengthen documentation, and develop new automations and internal tools based on evolving business needs.

If you’re comfortable stepping into an existing Python environment, 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 and production environment
• Understand current:
• Applications
• Automations
• Data pipelines
• Integrations
• Dependencies
• Deployment processes
• Identify undocumented workflows and system dependencies
• Build sufficient technical context to independently maintain and extend the environment
• Ensure continuity throughout the engineering transition

Production Support & Maintenance

• Monitor and maintain existing production systems
• Troubleshoot live issues and identify root causes
• Resolve bugs and operational failures efficiently
• Ensure existing applications and automations remain stable
• Investigate failed jobs, integrations, and unexpected system behavior
• Raise technical risks early and communicate issues clearly
• Prioritize reliability while making changes to existing systems

Data Pipelines & Automation

• Maintain, debug, and improve existing 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
• Ensure automated workflows remain observable and dependable

APIs & Integrations

• Maintain and extend existing APIs and third-party integrations
• Work with:
• REST APIs
• Authentication flows
• Webhooks
• Third-party services
• Troubleshoot integration and authentication failures
• Maintain reliable data flow between internal and external systems
• Extend integrations as business requirements evolve

Technical Debt & System Reliability

• Identify:
• Fragile systems
• Undocumented dependencies
• Technical risks
• Maintenance bottlenecks
• Prioritize technical debt based on business and operational impact
• Refactor and improve existing systems over time
• Reduce unnecessary complexity where appropriate
• Improve system reliability without disrupting production workflows

Documentation & Knowledge Management

• Create and maintain:
• Technical documentation
• Workflow diagrams
• Dependency maps
• Operating procedures
• Document systems as you learn and modify them
• Ensure important technical knowledge is not dependent on a single individual
• Keep documentation current as systems and workflows evolve

Internal Tooling & Development

• Build practical internal software and automation tools based on business needs
• Translate operational requirements into technical solutions
• Scope new internal tools with client stakeholders
• Extend existing systems where appropriate rather than unnecessarily rebuilding them
• Balance new development with ongoing production support and maintenance

Engineering Practices

• Use Git and pull requests for version control and code review
• Write and maintain automated tests where appropriate
• Maintain clear changelogs and documentation
• Push changes through 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 problem-solving skills
• Strong written English communication skills
• Strong technical documentation capabilities
• 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
• Strong understanding of data pipeline reliability and monitoring
• Experience working with scheduled jobs and automation workflows
• Familiarity with staged production deployments
• Experience working directly with non-technical business stakeholders

Tools & Technology

Python | SQL | Relational Databases | REST APIs | Webhooks | Authentication | Git | Pull Requests | Automated Testing | Data Pipelines | Automation Tools | Staging & Deployment Workflows | 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 troubleshoot production problems methodically
• Enjoy building automations that eliminate repetitive work
• Understand how data pipelines, APIs, and integrations fit together
• Document systems as you work rather than treating documentation as an afterthought
• 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 disciplined testing and staged releases over making untested production changes
• Take ownership of systems rather than waiting for someone else to tell you what needs attention

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 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, your focus will increasingly shift toward new development, including building automations, improving pipeline reliability, and scoping internal tools with the client.

Communication will happen primarily through Slack, with planning sessions and check-ins overlapping with U.S. business hours.

In short: you take ownership of an existing Python environment, keep production systems reliable, and progressively improve the automations, pipelines, integrations, and internal tools the business depends on.

Key Metrics for Success

• Smooth knowledge transfer and ownership of the existing Python environment
• Production system stability and reliability
• Successful completion of scheduled automations
• Reduced recurring pipeline and integration failures
• Faster resolution of production issues
• Improved technical documentation coverage
• Reduction in fragile or undocumented dependencies
• Reliable API 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
• Opportunity to work across Python, 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

1. Initial Application
2. Spark Hire One-Way Video Interview
3. Video Interview Screening
4. Client Interview
5. 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 have strong professional Python experience, have maintained production data pipelines and automations, and are comfortable taking ownership of an existing codebase, we’d love to hear from you.

Apply today and take ownership of the systems, integrations, and automations that keep critical business operations running.

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