Job Opportunities API

About JustPark JustPark is the premier partner offering both B2B solutions for destinations and B2C services for drivers, giving us the best of both worlds. We simplify the entire parking experience. From venues and councils to private driveways, our platform makes it simple for drivers to find, book, and pay for parking, while empowering our operating partners to deliver exceptional parking experiences. We've always believed parking should be easier, from end to end. That's why we, two trailblazing companies - ParkHub and JustPark - have come together to make that vision a reality. ParkHub revolutionized event parking in the US, optimizing venue operations for a smoother, stress-free experience. JustPark transformed parking in the UK, turning the hunt for a spot into a simple, seamless task. Now, as one unified company, we're combining expertise to offer a full-service, frictionless parking solution for both businesses and consumers. About the Role The Analytics Engineer is a hands-on technical role within JustPark's data team, sitting at the intersection of data engineering and analytics. You'll be responsible for building and maintaining clean, well-tested data models that power reporting, BI, and business decision-making across our two-sided marketplace. Working closely with the Data Platform and BI teams, you'll help translate raw data into trusted, scalable assets that shape how JustPark makes decisions. This is an ideal role for someone who is fluent in dbt and BigQuery, enjoys owning modelling problems end-to-end, and can hit the ground running in a fast-moving product environment. Responsibilities Build, test, and document dbt models in BigQuery across the staging, intermediate, and mart layers that power analytics and reporting across the business Partner with the BI team and Data Platform Lead to turn business questions into reusable, well-modelled data assets Work with backend engineers to stay ahead of schema changes and resolve data quality issues at source rather than patching them downstream Improve the warehouse over time — reducing complexity, improving query performance, and keeping BigQuery costs in check Contribute to data governance: documentation, naming standards, discoverability, and exploring how emerging AI tooling can strengthen the data platform What does success look like in your first 3 months? You'll have a solid understanding of the existing dbt project, data models, and key reporting areas, and is working independently on modelling tasks with minimal direction You shipped well-structured, tested dbt models that analysts and stakeholders can trust and use, and identified at least one area of the warehouse to meaningfully improve You built effective working relationships with the BI team, Data Platform Lead, and backend engineers, staying ahead of schema changes rather than reacting to them Documentation and governance contributions are in place, making it easier for the team to build on the work going forward Requirements Must-haves 4+ years of hands-on dbt Core experience in a production environment Proficient in SQL and comfortable with modern data warehouse concepts such as dimensional modelling and layered architecture Practical cloud data warehouse experience, with the ability to write efficient queries and a solid understanding of how to improve performance Translates business requirements into delivery, comfortable working from a business problem through to a shipped model Understands the distinction between transactional and analytical systems, and how backend schema changes can affect downstream data models Comfortable working in a git-based development workflow, including pull requests, code reviews, and contributing to team coding standards Takes ownership of data quality in the models they build, proactively identifying and resolving issues rather than treating it as someone else's problem Curiosity about AI applications in data, inclu