Staff Python Engineer (Data)

Arrive; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Flexible working

Hybrid work option

25 Days holiday entitlement

Private medical health insurance

Wellbeing programmes

Discounted gym membership

Mental health support (Blue Call)

Enhanced maternity/paternity

Pension scheme 5% by Arrive, 3% by you

Charity leave

Job summary

Arrive, the global mobility platform behind Parkopedia, is transforming urban travel. As a Staff Python Engineer you will lead data systems, design scalable data services, and shape production AI capabilities.

You’ll own the Spark-based pipelines, drive engineering standards, collaborate with data scientists, and evolve infrastructure-as-code in AWS to deliver reliable real-time insights.

Qualifications

• Extensive history of building and scaling data-intensive applications in production.
• Expert-level Python and its data ecosystem (NumPy, Pandas).
• 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.
• 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.

Responsibilities

• Architect and Implement: Own the technical roadmap for Spark-based data processes and optimize Airflow pipelines.
• Drive Engineering Excellence: Define standards for efficient, testable Python code across the org.
• Bridge Strategy and Execution: Collaborate with Data Scientists to translate models into production services.
• Modernize Infrastructure: Evolve AWS IaC and CI/CD pipelines for reliable deployments with minimal downtime.
• Advance Data Capabilities: Lead platform enhancements like feature stores and automated data monitoring.
• Scale AI Practices: Drive adoption of AI throughout the software development lifecycle.

Skills

Python

NumPy/Pandas

Apache Spark

Distributed data processing

Linux

Docker

AWS

Code reviews & pair programming

Tools

Docker

AWS

Airflow

Spark

Job description

We’ve signed up to an ambitious journey. Join us!

As Arrive, we guide customers and communities towards brighter futures and more livable cities, it isn’t a challenge just anyone could take on. Luckily, we have something to help us make it happen. Our people and our values. We Arrive Curious, Focused and Together. Just as our entire brand is inspired by the North Star, the shining light leading travelers to their destinations since time began, our values guide us. They help us be at our best. For our customers. For the cities and communities we serve. For ourselves. As a global team, we are transforming urban mobility. Let’s grow better, together.

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.

The Role

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. The Data Science Products team ingests billions of complex sensor data points daily to produce advanced machine learning products that help millions of drivers park or charge electric vehicles. For example, we predict the availability of parking in cities across the world and provide drivers with routes that reduce the time they will spend searching for a space near their destination.

This is a software engineering IC role focused on data. We are looking for an expert in Python and distributed systems, who knows how to design, build, and scale data services and architectures. You will influence technical strategy and promote best practices amongst engineers and data scientists.

How to make an Impact

• Architect and Implement: 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.

• Drive Engineering Excellence: 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.

• Bridge Strategy and Execution: 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.

• Modernize Infrastructure: 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.

• Advance Data Capabilities: 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.

• Scale AI Practices: Lead the adoption of AI throughout the software development lifecycle, evolving our internal coding practices while ensuring systems remain reliable and maintainable.

About you

You are a collaborative engineering leader who takes pride in having a big cross-team impact. You relish the strategic challenge of system design while still solving technically complex problems through writing code. You are passionate about building robust systems that solve real-world challenges and value the autonomy of leading large technical initiatives.

Requirements

• 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.

Benefits

You can expect great employment benefits such as:

• Flexible working - hybrid home and office-based opportunities.

• Paid Leave if you participate in an event for Charity.

• 25 Days holiday entitlement.

• An enhanced Workplace Pension Scheme - 5% by Arrive, 3% by you.

• Private Medical Health Insurance.

• Fantastic wellbeing programmes, including On-site Sports massages, Reiki and Head massages every week.

• Discounted gym membership.

• Access to Blue Call, a mental health support platform.

• Enhanced Maternity and Paternity offering.
  

About Arrive

Arrive, including brands like EasyPark, Flowbird, RingGo, ParkMobile and Parkopedia, is a leading global mobility platform. Present in over 90 countries and 20,000 cities, the company helps people and decision-makers make smarter decisions about urban mobility and ease the experience of travel worldwide. Arrive delivers a unique combination of the core ingredients to make cities more livable: from smart payments and optimized car parks to data-driven traffic reduction and support for reinvestment in public transport and green space. It’s about more than function, it’s about saving time and simplifying the experience of travel for everyone. Travel is more than a journey, it’s how you Arrive.