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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📌 Embedded Python Data & Automation Engineer (Ciudad de México)
🏢 Pavago
📍 Ciudad de México