Python engineer in data

One of the key brands within the Arrive is Parkopedia. Parkopedia is the world’s leading connected car services provider, used by millions of drivers and organisations such as Apple, Here, TomTom, and 20 automotive brands ranging from Audi to Volkswagen. Its mission is to provide the best in-car data and transaction services, to make mobility ecological, efficient and convenient. As a Staff Python Engineer, you will be a technical leader in our Data Science Products team and in the Data, Analytics and AI organisation of Arrive.

• Own the technical roadmap for our Spark-based data processes to ensure our Airflow pipelines are performant, cost-effective, and scalable.
• You will enhance our existing services through hands-on development and solve any complex performance bottlenecks, concurrency issues, and systemic bugs.
• Define standards for efficient, testable and reusable Python code across the organisation that ensure our services remain reliable, robust, and easy for other engineers to extend.
• Partner with Data Scientists to translate modeling requirements into high-performance production services.
• Your data architectures and pipeline designs will directly affect our real-time prediction APIs, so you'll need to design with downstream latency, freshness, and reliability in mind.
• Evolve our infrastructure-as-code (AWS) and CI/CD pipelines to keep up with cutting-edge approaches.
• You will personally contribute to the automation and observability patterns that allow us to deploy fresh data and production services with high confidence and zero downtime.
• Lead the hands-on development of platform enhancements, such as establishing feature stores for machine learning and building automated data monitoring systems to ensure data integrity and model reproducibility.
• Lead the adoption of AI throughout the software development lifecycle, evolving our internal coding practices while ensuring systems remain reliable and maintainable.

• Extensive history of building and scaling data-intensive applications in production, with a track record of leading technical initiatives from conception to deployment.
• Expert-level Python and its data ecosystem (Numpy, Pandas), including designing frameworks for data tasks.
• Deep understanding of distributed data processing engines like Apache Spark.
• Ability to reason about the full data lifecycle from ingestion through to real-time serving - your system designs will need to meet downstream API performance and data freshness requirements.
• A strong command of Linux, containers (Docker), and infrastructure as code for cloud deployments (AWS preferred).
• A passion for elevating engineering standards through pair programming and detailed code reviews to help other engineers grow their technical depth.

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