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описание
Arrive is a global mobility platform that helps people and decision-makers make smarter urban mobility choices and simplifies travel. Its services include smart payments, optimized car parks, data-driven traffic reduction, and support for reinvestment in public transport and green space. Parkopedia, one of its brands, provides connected car services, in-car data, and transaction services.
задачи
Own the technical roadmap for Spark-based data processes and improve Airflow pipelines for performance, cost efficiency, and scalability
Enhance existing services through hands-on development and resolve complex performance bottlenecks, concurrency issues, and systemic bugs
Define standards for efficient, testable, and reusable Python code across the organisation
Partner with Data Scientists to translate modeling requirements into high-performance production services
Design data architectures and pipelines with downstream latency, freshness, and reliability requirements in mind
Evolve AWS infrastructure-as-code and CI/CD pipelines
Develop automation and observability patterns for reliable, zero-downtime deployment of fresh data and production services
Develop platform enhancements, including feature stores for machine learning and automated data monitoring systems for data integrity and model reproducibility
Lead the adoption of AI throughout the software development lifecycle and evolve internal coding practices while maintaining reliable, maintainable systems
требования
Extensive experience building and scaling production data-intensive applications
Track record of leading technical initiatives from conception to deployment
Expert-level Python and its data ecosystem, including Numpy and Pandas
Experience designing frameworks for data tasks
Deep understanding of distributed data processing engines such as Apache Spark
Ability to reason about the full data lifecycle, from ingestion to real-time serving, and meet downstream API performance and data freshness requirements
Strong command of Linux, Docker, and infrastructure as code for cloud deployments; AWS is preferred
Passion for elevating engineering standards through pair programming and detailed code reviews
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