Python Data Engineer

About the Client: Our client is building a high-scale operations management and logistical dispatch platform optimized for massive field environments. The platform orchestrates enterprise workforce planning, automated staff rostering, real-time vehicle/field routing, and complex SLA-driven performance analytics. Built on high-performance mathematical solvers and distributed cloud systems, this engine helps operations teams maximize field efficiency and lower resource overhead under dynamic conditions.

Position Overview

We are seeking a deeply technical, algorithmically driven Python Data Engineer to completely own the core engine processing layer of our field operations platform. This is not a typical web backend track. You will design, build, and optimize constraint-satisfaction algorithms, real-time event-driven dispatch components, and high-throughput data processing pipelines. Your primary mandate will be translating workforce schedule rules, real-time geographic location streams, and third-party HR systems data into highly efficient operational schedules.

Key Responsibilities

• Constraint Planning Engine: Process massive volumes of scheduling variables through rule evaluation matrices and engagement profiles using Celery, Polars, and Google OR-Tools.
• Automated Rostering Logic: Build and scale serverless solvers running as Azure Functions that generate staff rosters based on employee certifications, fatigue laws, and leave data parsed from external HR APIs.
• Real-Time Dispatch Optimization: Engineer an always-on proximity scoring engine using live hardware geolocation streams and attendance tracking metrics to assign real-time field tasks gracefully.
• Performance Pipeline Aggregation: Construct timer-triggered background workers to run scheduled KPI aggregation algorithms and calculate precision SLA scores across all operational modules.
• Architecture Decoupling Management: Enforce clean architectural boundaries by writing structured, verified outputs into Redis queues and Azure SQL systems via SQLAlchemy, keeping your data workflows decoupled from the frontend API layers.

Required Skills & Qualifications

• 5+ years of verified professional history running Python backend engineering, data engineering, or quantitative algorithm design.
• Demonstrated production experience handling combinatorial optimization solvers, scheduling algorithms, or constraint satisfaction libraries (e.g., OR-Tools, PuLP).
• Strong data processing capabilities manipulating large-scale datasets using high-performance libraries like Polars, Pandas, or Apache Spark.
• Hands-on system implementation familiarity with task distribution queues and async workers like Celery with a Redis broker.
• Solid experience working within Microsoft Azure cloud infrastructure and writing high-performance SQL relational database queries.
• Location Context: 100% remote-first operational infrastructure flexibility open exclusively to qualified engineering professionals permanently residing in Sri Lanka, India, or Pakistan.

Preferred Strategic Indicators (Nice to Have)

• Experience deploying event-driven applications on Azure Functions or equivalent serverless ecosystems.
• Familiarity with data schema enforcement and strict runtime validations using Pydantic.
• Knowledge of machine learning model execution paths (scikit-learn, ONNX, or Azure ML) to support future predictive model migrations.

What We Offer

• The exceptional technical freedom to build a mission-critical, rule-based algorithmic engine that serves as the foundation for future ML optimization models.
• Highly competitive international compensation package calibrated against engineering experience and optimization depth.
• A pure, focused backend engineering track completely isolated from UI, frontend, or TypeScript requirements.
• Flexible work-from-home parameters aligned seamlessly with your local South Asian timezone infrastructure.