Data Engineer, Python

🕒 May 24

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Flatgigs

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2023

🎯 Recruiter

👥 HR Tech

Recruitment • HR Tech

Flatgigs is a Dubai-based talent and recruitment firm that partners with investor-backed and investor-ready MENA startups to fill critical revenue-driving roles. They provide end-to-end strategic talent acquisition, performance-focused cultural fit hiring, hyper-accelerated onboarding, and data-driven hiring optimization to maximize hiring ROI and extend runway for startups. Flatgigs positions itself as a B2B partner for high-growth companies and investors, embedding experienced leaders and teams to drive faster revenue milestones and reduce hiring risk.

📋 Description

• Own the Python engine layer for algorithms and data processing • Process large volumes of operational schedule data through constraint evaluation • Generate periodic staff rosters by solving constraint problems • Real-time task assignment using proximity scoring against live employee location data • Scheduled KPI aggregation and SLA scoring across all modules

🎯 Requirements

• 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 • 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