Flatgigs

Information Technology and Services

PythonDataEngineer

Amherst, Nova Scotia, Canada Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Python Data Engineer at Flatgigs. Skills: Python, Data Processing, Constraint Solving, Azure. Own the Python engine layer. Develop algorithms and data processing”

What You'll Achieve.

Produce workforce planning demands; Generate periodic staff rosters; Assign tasks in real-time; Aggregate KPIs and score SLAs; Write clean, reliable outputs into Redis and Azure SQL

Industry & Context.

Information Technology and Services
Problems you'll solve

Constraint solving; Optimization; Scheduling algorithms

What They're Looking For.

Must Have

5 or more years of Python backend or data engineering experience, Experience with constraint solving, optimization, or scheduling algorithms (OR-Tools, PuLP, or equivalent), 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

Nice to Have

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, 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'll Do.

Own the Python engine layer

Develop algorithms and data processing

Power core operational workflows

Process large volumes of data

Evaluate constraints and business rules

Produce workforce planning demands

Generate periodic staff rosters

Solve constraint problems

Assign tasks in real-time

Aggregate KPIs and score SLAs

Build constraint-based algorithms

Develop ML-driven prediction models

How You'll Work.

Team & Collaboration

Work with product teams; Integrate with third-party HR systems; Async message queuing between modules; Consume from Redis and Azure SQL

Full 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

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