Python Data Engineer - Workforce Planning Platform

About the role

A client of Flatgigs is building an operations management platform for workforce planning, rostering, dispatching and performance analytics in a large-scale field operations environment. You will own the Python engine layer, which powers the algorithms and data processing behind the core workflows used daily by operations teams. This is not a typical web backend role: you will work on constraint-based planning algorithms, large-scale data pipelines and real-time dispatch engines.

Key responsibilities

• Build the planning engine that processes large volumes of schedule data through constraint evaluation, business rules and engagement profiles to produce workforce planning demands (Celery, Polars, OR-Tools on Azure App Service)
• Build the rostering engine that generates periodic staff rosters by solving constraints against planning demands, employee certifications and leave data from third-party HR systems (Azure Function App)
• Build the dispatch engine for real-time task assignment using proximity scoring against live location, attendance and schedule data (Azure Function App or App Service)
• Build the performance engine for scheduled KPI aggregation and SLA scoring (timer-triggered Azure Function App)
• Integrate third-party HR and workforce management APIs and handle real-time location data streams

Required profile

• 5+ 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 and comfortable working with SQL databases and performant queries
• Experience consuming REST APIs and message queues

Required skills

• Python 3.11
• Celery with Redis, Polars, OR-Tools, Azure Functions, Azure SQL, Azure Blob Storage, Azure Service Bus
• Preferred: Azure Functions, scikit-learn or ONNX/Azure ML, Pydantic; nice to have: large-scale workforce management systems, SLA frameworks, fast-paced operational environments