Flatgigs

Flatgigs

FlatGigs tailors recruitment strategies for flat-structured companies, aligning teams with skills and culture. Utilizing tech tools for precise candidate organization.

Professional Services

Description

  • Own the Python engine layer that powers core operational workflows for planning, rostering, dispatch, and performance analytics.
  • Build and maintain the planning engine that evaluates operational schedules, business rules, and engagement profiles to generate workforce planning demands.
  • Develop constraint-based rostering logic that creates periodic staff rosters using planning demands, certifications, and leave data from external HR systems.
  • Implement a real-time dispatch engine that assigns tasks using proximity scoring, live location data, attendance records, and operational schedules.
  • Create and maintain a performance engine that aggregates KPIs and calculates SLA scores across the platform on a scheduled basis.
  • Process large-scale operational data through event-driven and batch workflows using Celery, Azure Functions, and related infrastructure.
  • Integrate data from third-party HR and workforce management APIs, message queues, and real-time location streams.
  • Persist clean, reliable outputs to Azure SQL and manage intermediate files in Azure Blob Storage.
  • Collaborate with surrounding platform services by publishing and consuming data through Redis and Azure SQL boundaries.

Requirements

  • 5 or more years of Python backend or data engineering experience.
  • Experience with constraint solving, optimization, or scheduling algorithms such as OR-Tools, PuLP, or equivalent.
  • Strong data processing experience with large datasets using Polars, pandas, or Apache Spark.
  • Experience with task queues and async processing such as Celery, RQ, or equivalent.
  • Familiarity with Azure and Azure-based serverless or app-hosting services.
  • 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.
  • Preferred: experience with OR-Tools specifically.
  • Preferred: knowledge of scikit-learn or ML integration tools such as ONNX or Azure ML for future model support.
  • Preferred: experience with Pydantic for data validation and schema enforcement.
  • Nice to have: experience with large-scale workforce management or scheduling systems.
  • Nice to have: familiarity with SLA-based operational frameworks.
  • Nice to have: experience working in fast-paced, operationally critical environments.
  • This is a pure Python engineering role with no expectation to write TypeScript, work on the API layer, or contribute to the frontend.

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