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Data pipelines
Extraction, transformation, and loading with explicit schemas, idempotent runs, late-arriving data handled deliberately, and backfill as a supported operation rather than a manual edit.
SERVICE · PYTHON
Python Engineering
Python is where most data work and most automation naturally lands, and it is where a lot of it quietly stays as a script on somebody's machine. We turn that work into scheduled, monitored, tested services — with the same expectations about failure handling and observability that any other production system gets.
THE PROBLEM
The pipeline runs on one laptop, and everybody knows whose.
Data and automation work grows organically because it starts small and useful. One notebook becomes a scheduled script; the script grows arguments; somebody adds a second one that depends on the first finishing; a third reads a file the second happens to leave behind. Nothing about this was a bad decision at the time. But it now has no tests, no schedule you can inspect, no alert when it silently produces nothing, and no way to re-run yesterday when the source system arrives late. The failure that matters is rarely a crash — a crash gets noticed. It is the run that completes successfully having processed nothing, and gets used for a week before anyone checks.
Signals you might recognize
A critical process depends on a script that lives on one person's machine.
When a pipeline fails, you find out from a report that looks wrong rather than from an alert.
Re-running yesterday's data means editing dates in a file by hand.
Nobody can say which version of the transformation produced last quarter's numbers.
WHAT WE DELIVER
What this actually includes.
Concrete engineering capabilities deployed as part of this service practice.
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Automation services
Scheduled and event-driven jobs with a visible schedule, run history, structured failure reporting, and alerts on the quiet failure — the run that succeeded and did nothing.
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FastAPI backends
Typed HTTP services with request validation, generated OpenAPI descriptions, async input and output where it earns its complexity, and authentication that matches your existing identity setup.
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AI integration layers
The service between your systems and a model provider: prompt assembly, retrieval, response validation, retries, cost accounting, and a full record of what was sent and returned.
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Batch and scheduled processing
Long-running work with checkpointing, partial-failure recovery, resource limits, and progress that can be inspected while it runs rather than inferred from a log tail.
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Data quality checks
Assertions on row counts, distributions, nullability, and referential consistency, running as part of the pipeline so bad data stops rather than propagates into a dashboard.
HOW WE WORK
How we approach Python Engineering.
Milestone-driven delivery, so integration problems surface in week two rather than in week ten.
01
Map the flow that exists
Where the data comes from, what touches it, what depends on the output, and which steps are undocumented. Dependencies discovered in production are the ones that cause the incidents.
Stage 01 activities
- Source and destination inventory with owners named
- Dependency graph including the informal ones
- Volume, frequency, and lateness characteristics per source
- The list of consumers who will notice if the output changes
02
Define the contract at each boundary
Explicit schemas with typed columns and nullability, plus a stated policy for what happens when a source breaks its own contract. Silent coercion is how a data problem becomes a reporting problem three weeks later.
Stage 02 activities
- Schema definitions with types and nullability per source
- A policy for malformed rows: reject, quarantine, or fail the run
- Timeliness expectations and what late arrival triggers
- Versioning so a schema change is a decision rather than a surprise
03
Build it re-runnable
Every job is safe to run twice on the same input. That single property turns recovery from a careful manual exercise into re-running a command, and it is the difference between a bad morning and a bad week.
Stage 03 activities
- Idempotent writes keyed by partition or business key
- Backfill as a first-class supported operation
- Checkpointing so a long run resumes rather than restarts
- Deterministic transformation with the code version recorded per run
04
Add the checks that catch silence
Volume, distribution, and freshness assertions run as part of the job. The failure mode worth engineering against is not the crash — it is the successful run that produced nothing and was believed.
Stage 04 activities
- Row-count and distribution assertions against expected ranges
- Freshness checks that alert when data simply stops arriving
- Referential checks across related datasets
- Quarantine for rejected records, with a review path
05
Operationalize
Containerized, scheduled through something with a visible history, alerting to a channel somebody watches, and documented well enough that the person on call was not required to write it.
Stage 05 activities
- Container packaging with pinned, auditable dependencies
- Orchestration with visible run history and retry policy
- Alerts routed to a monitored destination, not an inbox
- Runbooks covering the recurring operational tasks
TECHNOLOGY DEPTH
Technologies behind Python Engineering.
The libraries, runtimes, and services we standardise on for this work, and keep patched.
Backend
- Python
Data
- PostgreSQL
- MySQL
- Microsoft SQL Server
- Redis
- Vector search
Cloud
- Docker
- AWS
- Azure
AI
- OpenAI
- Anthropic
DELIVERABLES & OUTCOMES
What you receive, and what actually changes.
We measure success by durable working software in your possession, not presentation slides.
What you get
Documented pipeline definitions with schemas at every boundary
Idempotent jobs with backfill and checkpointed recovery
Data quality assertions running as part of each pipeline
FastAPI services with generated OpenAPI descriptions where an interface is needed
Container images with pinned dependencies and reproducible builds
Scheduling, alerting, and runbooks for the operational tasks
What changes
Pipelines that run somewhere inspectable rather than on a named person's machine
Failures that alert, including the quiet ones that produce empty output
Re-running a past period as a routine command instead of a careful manual exercise
Traceability from a number in a report back to the code version that produced it
QUESTIONS & ANSWERS
Questions about Python Engineering.
Direct answers to common technical and engagement questions.
Not always. For a handful of jobs, a scheduler with visible run history and good alerting is enough, and a full orchestration platform is a system you now have to operate. For dozens of interdependent jobs, the picture reverses. We size the tooling to the actual dependency graph rather than to the one you might have later.
Yes. Notebooks are good for exploration and poor for production, so the usual path is to extract the logic into tested modules the notebook can still import. The analyst keeps working the way they prefer, and the scheduled path no longer depends on cell execution order.
Deliberately, and it is decided before the first line of code. Either the pipeline waits within a defined window, or it processes what is present and reconciles on a later run. What we avoid is the third option, where late data silently produces a wrong number that nobody reconciles.
For most business data volumes, yes — the bottleneck is nearly always the database or the network, not the interpreter. Where it genuinely is not, the fix is usually to push work into the database or use a columnar processing library, not to rewrite the pipeline in another language.
Yes, and they are frequently the same project. The data pipeline produces what a retrieval layer indexes, and the service that calls a model needs the same treatment as any other integration: validation, retries, cost accounting, and a record of what was sent.
Have something ambitious to build?
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.