Python Data Engineer

Job Description

Our client is building an operations management platform that handles workforce planning, rostering, dispatching, and performance analytics for a large-scale field operations environment. You will own the Python engine layer, responsible for the algorithms and data processing that power the core operational workflows used daily by operations teams.

This is not a typical web backend role. You will be working on constraint-based planning algorithms, large-scale data processing pipelines, and event-driven dispatch engines that operate in real time against live operational data.

WHAT YOU'LL OWN:

• Planning engine: processing large volumes of operational schedule data through constraint evaluation, business rules, and engagement profiles to produce workforce planning demands. Built on Celery, Polars, and OR-Tools running on Azure App Service
• Rostering engine: generating periodic staff rosters by solving constraint problems against planning demands, employee certifications, and leave data pulled from third-party HR systems. Running as an Azure Function App
• Dispatch engine: real-time task assignment using proximity scoring against live employee location data, attendance records, and operational schedule data. Running as an always-on Azure Function App or App service.
• Performance engine: scheduled KPI aggregation and SLA scoring across all modules. Running as a timer-triggered Azure Function App

WHY'S THIS ROLE INTERESTING:

The planning engine is the core of this platform. The algorithm you build determines how efficiently an operations team deploys its workforce against a complex schedule of demands. When the platform matures, the rule-based constraint engine you build will become the foundation for an ML-driven prediction model. You are building something that runs in a live operational environment and directly impacts how a team performs on the ground every day.

WHAT YOU'LL WORK WITH:

• Python 3.11
• Celery with Redis as broker for long-running jobs
• Polars for large-scale data processing
• OR-Tools for constraint solving and optimisation
• Azure Functions for event-driven and scheduled workloads

• Azure SQL via SQLAlchemy for output persistence
• Azure Blob Storage for intermediate file handling
• Azure Service Bus for async message queuing between modules
• Third-party HR and workforce management API integrations
• Real-time location data streams for proximity-based task assignment

Requirements

WHAT WE ARE LOOKING FOR:

Required:

• 5 or more years of Python backend or data engineering experience
• Experience with constraint solving, optimization, or scheduling algorithms (OR-Tools, PuLP, or equivalent)
• Strong data processing experience with large datasets (Polars, pandas, or Apache Spark)
• Experience with task queues and async processing (Celery, RQ, or equivalent)
• Familiarity with Azure
• Comfortable working with SQL databases and writing performant queries
• Experience consuming REST APIs and message queues

Preferred:

• Experience with Azure Functions or equivalent serverless compute
• Experience with OR-Tools specifically
• Knowledge of scikit-learn or ML model integration (ONNX, Azure ML) for a future ML upgrade path
• Experience with Pydantic for data validation and schema enforcement

Nice to have:

• Experience with large-scale workforce management or scheduling systems
• Familiarity with SLA-based operational frameworks
• Experience working in fast-paced, operationally critical environments

WHAT YOU "DO NOT NEED" TO KNOW:

This is a pure Python engineering role. You will not be expected to write TypeScript, work on the API layer, or contribute to the frontend.

Your responsibility sits clearly within the Python processing layer. The integration boundary between your work and the wider platform is Redis and Azure SQL. You will write clean, reliable outputs into these systems, and the surrounding services will consume them from there.

In simple terms: you own the Python logic and data processing. The rest of the platform reads from what you produce.