Data Platform Engineer

Runna
London
2 weeks ago
Create job alert

We're putting together a talented team to build the #1 training platform for Runners.

We help everyday runners become outstanding by providing world-class training, coaching and community for everyone, whether you're improving your 5k time or training for your first marathon. To date we have built iOS, Android and Apple watch apps that help people achieve their goals by coaching them through the full journey and syncing to their favourite fitness devices.

We're growing extremely fast and in November 2023 closed a new £5M funding round led by JamJar with participation from Eka Ventures, Venrex and Creator Ventures. In 2024, we were selected by Apple as one of three global finalists for the 2024 iPhone App of the Year reflecting the innovation and impact of what we've built.

We want to grow as fast as we can into the future and are looking for individuals who will help us get there. For more about our background and growth check out our Careers Page!

We're now looking ahead to the future and the people who want to help us build and scale Runna. Our aim is to reach millions of subscribers in the next 5 years and be the go-to training platform for any runner. Now is a magical time to join, we're still small, and everyone makes a foundational difference.

Who We're Looking For

We are looking for a talented, creative, and positive team player to join our highly skilledcross-functional engineering teamand lead the development of our data platform. As part of this work, you'll be working closely with the engineering, product and growth team to build the foundations of how we ingest, process, store and query all the data we receive each day and use it to drive all of Runna's data and analytics needs (including machine learning). You will work closely with our founders and CTO to help shape the future of Runna, who will support you all along this exciting journey.

Leading the data platform, you'll help build the #1 running app in the world, pioneering the way that people train and use fitness apps.

As a Data Platform Engineer your role will include:

  • Architect, build, test, and deliver a state-of-the-art data platform to support the data needs of our rapidly growing company.
  • Design and implement scalable and efficient data pipelines, ETL processes, and data integration solutions to collect, process, and store large volumes of data within AWS.
  • Implement data transformation logic to cleanse, validate, and enrich raw data for analysis and consumption by downstream applications.
  • Further our integration with Mixpanel to enable advanced analytics and data tracking, providing insights into user behaviour and product performance.
  • Adopt a data platform mindset by designing and developing data pipelines that prioritise security, scalability, uptime, and reliability.
  • Mentor and guide team members on data engineering best practices and the use of AWS and Mixpanel.
  • Collaborate with cross-functional teams, including product, growth, engineering, and business stakeholders, to ensure the data platform aligns with company goals and drives value.
  • Continuously evaluate and adopt new technologies and tools to enhance the data platform's capabilities and performance.
  • Communicate the advantages and limitations of technology solutions to partners, stakeholders, and team members.

Requirements
What experience we're looking for
If you don't quite meet all of the below skills, we'd still love to hear from you as we might be able to tweak the role slightly or offer you a position better suited for you. You can apply directly below or contact us if you're still unsure.

Your key experience:

  • 2+ years in a Data Platform role or similar.
  • 1+ years working with AWS.
  • You've led the development of key projects within a data platform team.
  • Experience with quantitative methods and approaches to solving problems gained through various experiences or studies (e.g., Computer Science, Mathematics, Physics, Engineering degree or equivalent practical experience).

Your key skills:

  • Experience with delivering data pipelines within AWS.
  • Proficiency with Python programming.
  • Familiarity with AWS Redshift or similar cloud DWH platforms such as Snowflake or BigQuery.
  • Proficiency with SQL and experience with relational databases (e.g. Amazon Redshift), NoSQL databases (e.g. DynamoDB), and graph databases (e.g. Amazon Neptune).
  • Experience with infrastructure as code tools (e.g. CloudFormation, Terraform) and CI/CD pipelines.
  • Experience with observability and monitoring tools (e.g. Cloudwatch, Datadog).
  • Analytical and detail-oriented, with a commitment to producing high-quality work.
  • A pragmatic mindset, with excellent communication and collaboration skills.
  • Able to work within a highly-skilled engineering team in a fast-paced, iterative environment.
  • Enthusiasm for our ways of working which include:
    • Iterative development, continuous deployment and test automation.
    • Knowledge sharing, pair programming, collaborative design & development.
    • Shared code ownership & cross-functional teams.

Bonus points if you:

  • Have experience with Serverless architectures.
  • Experienced with job orchestration frameworks (e.g. Airflow, MWAA on AWS).
  • MLOps knowledge and grasp of basic concepts.
  • Have a strong interest in the health/fitness technologies.

Our tech stack
Check out our tech radar here which we are constantly iterating, and below you can find a small reflection of our current tech stack:

Frontend:

  • React Native (iOS and Android).
  • Typescript.
  • GraphQL (Apollo Client).
  • Fastlane.
  • SwiftUI (Apple Watch).
  • Maestro E2E tests.

Backend:

  • Serverless (AWS).
  • Lambdas (NodeJS & Python).
  • AWS AppSync.
  • DynamoDB, S3, SQS, SNS, EventBridge, SageMaker.
  • Postman API tests.

All the other good stuff:

  • Sentry.
  • GitHub Actions.
  • Intercom, Mixpanel.
  • RevenueCat.
  • App Store Connect / Play Store.
  • Figma.

Benefits
Data Platform Engineer Interview Process
Our aim is to keep the interview process as straightforward and enjoyable as possible, and will consist of the following stages:

  • Kick off! (apply below).
  • Please let us know if there's anything we can do to better accommodate you throughout the interview process - this can be from scheduling interviews around childcare commitments to accessibility requirements. We want you to show your best self in the process.
  • Introductory chat (25-minute video call).
  • Take home technical task (max 1-2 hours to complete).
  • 1.5-hour technical interview (the first half of the call will be used to discuss the take-home technical task from the previous stage and the second half will be some general architecture/tech questions).
  • Meet the team and in-person chat (in-person chat with founder(s), rest of the team and technical discussion).

Once the process is finished, we promise to let you know our decision as soon as possible.

We offer a salary of £60,000 - £100,000 (depending on experience), plus equity in the form of Runna stock options.

  • Based on years of direct, relevant experience. Software Engineer I £42.5k, Software Engineer II £47.5-60k, Software Engineer III £60-80k, Software Engineer IV £80k-95k, Software Engineer V £95k+.

We'll be growing our package of benefits over time. We currently offer:

  • Flexible working (we typically work 2-3 days in our office in Vauxhall).
  • Salary reviews every 6 months or whenever we raise more investment.
  • 25 days of holiday plus bank holidays.
  • A workplace pension scheme where if you pay 5% we pay 3%.
  • A brand new Macbook, a running watch of your choice, and anything else you need to do your best work.
  • Private health insurance.
  • Enhanced family care policy (3 months fully paid leave when a new Runna joins the family, fertility support & other benefits).
  • An hour slot each week (during work time) to do a Runna workout.

At Runna we have a limited number of employment visas that we are able to sponsor and are limited by govt. guidelines so cannot guarantee a visa sponsorship to all applicants. Please do apply though as we will consider all applicants.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Engineering and Information Technology

Industries

Non-profit Organizations and Primary and Secondary Education

#J-18808-Ljbffr

Related Jobs

View all jobs

Data Platform Engineer

Data Platform Engineer

Data Platform Engineer

Data Platform Engineer

Data Platform Lead Engineer (Platform Essentials and AI enablement)

Platform DevOps Engineer Product & Technology · Remote, UK, Poland ·

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.