Python Development
Python development at OMAV covers automation, data pipelines and services, documented well enough to hand over. Most of the Python we are asked to rescue works correctly and cannot be maintained by anyone except the person who wrote it.
Automation that outlives its author
Python earns its place where the work is data movement, automation or glue between systems. It is quick to write, which is its strength and the reason so much of it becomes unmaintainable: the version that worked got scheduled, and nobody went back.
The typical engagement therefore starts with an inventory rather than a build. What runs, on what schedule, touching what, and which of it nobody dares change. That list is usually shorter than feared and more alarming than expected.
What we add is not sophistication. It is reproducible environments, tests around the paths that matter, type hints where they clarify intent, and a runbook that says what breaks and what to do about it.
What a Python engagement covers
Six workstreams, weighted toward maintainability.
Estate inventory
What runs, where, on what trigger, touching which systems, and who depends on the output — established before anything is rewritten.
Services and APIs
FastAPI services where an interface is needed, with request validation and typed responses rather than dictionaries passed by convention.
Data pipelines
Extraction, transformation and loading with validation at the boundaries and alerting on failure, so a silent partial load is not discovered a month later in a report.
Environment reproducibility
Dependency and environment management that reproduces on another machine, which is the single most common defect in inherited Python.
Tests and typing
Tests around the logic that matters and type hints where they make intent clear, so that a change can be made confidently by someone who did not write the original.
Runbook
Documentation covering what fails, how to tell, and what to do — written for whoever is on call rather than for whoever built it.
How a Python engagement runs
Inventory
Everything that runs catalogued with its trigger, dependencies and consumers, so decisions are made against the real estate.
Stabilise
Environments made reproducible and the highest-risk jobs given validation and alerting, before any rewriting begins.
Refactor or rebuild
Each item judged on its own merits — some are fine, some need a wrapper, a few need replacing — with the reasoning recorded.
Extend
New functionality added onto the stabilised base, with tests and typing from the start rather than retrofitted.
Hand over
Runbook, walkthrough and access, judged against whether an on-call engineer could act on an alert unaided.
Python next to the alternatives
When Python is the right tool and when it is not.
| Dimension | Python | Java | A platform tool |
|---|---|---|---|
| Best at | Data work, automation, glue | Long-lived transactional services | Standard integrations |
| Time to first version | Days | Weeks | Hours |
| Maintainability | Depends entirely on discipline | Enforced by the ecosystem | Vendor-maintained |
| Runtime cost | Low | Low | Licence per connection |
| Right when | The logic is bespoke | The service is core and long-lived | The integration is off the shelf |
| Wrong when | It becomes a core service by accident | The task is a script | Logic exceeds the tool |
What we will tell you plainly
Not every automation should be Python. Where an integration is genuinely standard, a platform connector maintained by someone else is cheaper over five years than code you own, and we will say so even though it removes work from our scope.
We also do the engineering around models rather than presenting ourselves as a data science practice. Pipelines, serving, monitoring and evaluation harnesses, yes. And where a rules engine would beat a model for your problem — which is more often than the market implies — we will recommend the rules engine.
- Start with an inventory of what actually runs, not with a rewrite plan.
- Irreproducible environments are the most common defect in inherited Python.
- Validate at pipeline boundaries and alert on failure; silent partial loads are the expensive ones.
- The runbook is written for the person on call, not for the person who built it.
Questions buyers ask about this
Can you take over an existing estate of scripts?
Yes, and it is the most common way these engagements start. We inventory what runs, what it touches and what nobody dares change, then stabilise the highest-risk items before rewriting anything. Rewriting first is how a working estate becomes a broken one.
Do you do machine learning?
We do the engineering around models: pipelines, serving, monitoring and evaluation. We are not presenting ourselves as a research practice. We will also tell you when a rules engine would outperform a model for your problem, which is more often than the current market suggests.
How do you make Python maintainable?
Reproducible environments, tests around the logic that matters, type hints where they clarify intent, a readable project structure, and a runbook. None of it is sophisticated. All of it is the difference between code your team can change and code they route around.
Python or Java for a new service?
Python where the work is data movement, automation or bespoke logic that will change often. Java where the service is core, long-lived and transactional. The failure mode to avoid is a Python script that becomes a core service by accident, without ever being given the treatment a core service needs.
What about scheduling and orchestration?
Whatever you already run, if it works — cron, Airflow, a cloud scheduler, or your CI system. We would rather use the tool your team already operates than introduce an orchestrator that becomes a second thing to maintain.
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Still not sure this is the right service?
Answer four questions and we will tell you which one fits — or that none of them do.
Tell us what you are trying to fix.
A short conversation about the objective, the constraints and the timing. If we are not the right fit, we will say so.