Data Engineers: The Hidden Engine Behind Race Performance

Setting up cars for a race weekend may seem like a job that is done a week or two before the race, however this is a set-up that takes several months, if not a year. One of the roles that assists with this set-up is the Data Engineer. A Data Engineer is an intriguing role in motorsport that many may not have heard of. We are fortunate to have very visible role models in this field - like TGR Haas Formula 1 Team’s Laura Müller. Data Engineers are essential in the world of motorsport, and we can’t wait to lift the lid on what it entails. 

What is the role of a data engineer?

A day in the life of a data engineer is rarely the same, and, depending on the size of the team, they may do different tasks. These can include using performance data gathered throughout race weekends to analyse and improve vehicle performance, helping with the set-up and calibration of any logged data, identifying problems in the vehicle systems, and helping the race engineer with communicating information to the driver. Sometimes, in certain teams and different feeder series, the race engineer and data engineer may have the same role, whilst in other teams, the data engineers work alongside the race engineers. For example, race engineer and Team Manager of Campos Racing S.L. in F1 Academy, Gabriela Parra Muñoz started off as a data engineer in Formula 3, and had experience in GT and WEC before moving to F1 Academy, where she became a Race Engineer for Carrie Schreiner in 2024. 

Gabriela Parra Muñoz started as a data engineer before becoming becoming Race Engineer and Team Manager for Campos Racing in F1 Academy.

Data Engineers will be dealing with many types of data to look after different parts of the car, including the tyres, powertrain, engine, oil temperatures, or any other sensors that can be found on the car. Across the season, this data is evaluated to help with set-up between similar tracks, and aids with overall performance of the car. 

A typical day for Gracie Talia Ross, a data and performance engineer in British GT and GT Cup teams, might include downloading, preparing and handling the data from sensors after the cars are brought back to the pits. During her time at Orange Racing, Gracie used the data that she downloaded to calculate fuel consumption calculations in order to optimise the fuel that was required for performance, and even created a fuel calculator on Excel to make the process easier. 

The graphs and conclusions that data engineers create allow drivers alongside their  engineers to compare data, find areas where time can be improved, and see how their performance varies over the course of a lap. 

What skills do data engineers need?

To excel in this role, Data Engineers need to be fluent in analysing data and confident using programming languages such as Python and C++ - which are often a standard requirement for any data or performance related roles in motorsport -  that help build scripts, software dashboards and telemetry tools. Knowing other programming languages and cloud platforms, such as SQL and AWS, which focus on data storage and management to handle the huge inflow of live lap data is also valued. Other software that helps visualise data includes Pi Toolbox and WinTAX, which are more industry-specific programs. Alongside being able to use language and telemetry systems, it is important to have a solid engineering knowledge covering vehicle dynamics and different mechanical structures, as well as being able to conduct reliability and pre-session checks. Understanding and knowing almost all the components and bits of the car can be just as important as being able to read and manipulate data – because if one does not understand what they are working with, they cannot answer and recognise why certain patterns in the data may appear.

Just as important as experience, data engineers must have great communication skills. They must be able to communicate clearly complex telemetry and software data to mechanics, race engineers and drivers during high-pressure conditions to allow for the best performance. 

How can you become a data engineer?

For aspiring students, it may seem  difficult to become a data engineer – however it is important to note that a data engineer is a common entry-level job before  continuing onto working as a race engineer, strategist or a performance optimisation engineer. 

There are many pathways into engineering - not all data engineers start as engineering or computing students, they may be maths and physics students, or find their way through programmes such as Formula Student.

Nicola Davies initially started as a PU Data Scientist before being promoted to a Data Strategy & Insights Team leader at Oracles Red Bull Racing.

University and personal projects handling data  are a great way to start your journey to becoming a data engineer. Motorsport teams love to see how someone may use aforementioned programmes - not just to create a script or plot the data, but the conclusions and evaluations that they make. Nicola Davies, Data Strategy & Insights Team Leader, worked initially at ASOS, using models that looked at financial and commercial insight before she started working at Oracle Red Bull Racing’s PowerTrains team in 2024, building data models that helped create  the team’s 2026 power unit, which just secured its first win at the 2026 Bahrain Grand Prix in Malaysia.

Many engineers and staff in motorsport have a background in Formula Student teams worldwide. As different Formula Student teams have different practices and sub-teams, students who may want to become a data engineer could  work in the Vehicle Dynamics team, who closely focus on the performance of the car and use the data from simulations and any testing to help with calibrating and making any set-up changes. However, not all students have to be part of the Vehicle Dynamics team to become a data engineer. Being part of the Chassis & Structures, Suspension or Aerodynamics sub-teams also involves a lot of data analysis during physical testing, or even simulations through different software.

For those who are not university students or did not have the chance to study similar subjects, there are many websites that can be found online where users have the chance to read telemetry data from race weekends and analyse the data on their own. Two examples of this are the FastF1 Python library, which gives access to F1 timing and telemetry data, and OpenF1, an open API for live and past F1 data. A small project, such as comparing two drivers' fastest laps and explaining where the time was gained, can give someone something real to talk about in an application.

A data engineer is not defined by their degree, but by being able to show that  you have the skills to  turn raw data into clear, defined conclusions. Combining that with a solid understanding of the car and good communication skills will allow for a good start towards your motorsport career journey.