Data Engineer

Data Engineer

(one day a week in the central London office)

Sports Analytics

This role is a unique opportunity for a

Data Engineer

to combine technical challenges with creativity in a collaborative, high-standard work environment.

By joining this team, you’ll not only be part of a creative and open work culture focused on innovation and excellence but also have the chance to work with and collaborate with some of the most well-known footballers in the industry.

This position offers significant opportunities for professional growth within sports analytics and the potential to impact sports performance through advanced technology, making it an ideal setting for those passionate about leveraging cutting-edge technology to make meaningful contributions in the world of sports analytics.

Key responsibilities for the role of Data Engineer include:

Design, construct, install, test, and maintain highly scalable data management systems.
Ensure systems meet requirements and industry practices for data quality and integrity.
Integrate data management technologies and software tools into existing structures.
Create data tools to support sport data modelling, prediction and analytics.
Work with data and analytics experts to strive for greater functionality in our data systems.

As the selected Data Engineer, your background will include:

3+ years

industry experience in a

Data Engineer

role and a strong academic background
Python & SQL:

Advanced-level

Python

for data applications and high proficiency

SQL

for complex querying and performance tuning.
ETL/ELT Pipelines:

Proven experience designing, building, and maintaining production-grade data pipelines using

Google Cloud Dataflow (Apache Beam)

or similar technologies.
GCP Stack:

Hands-on expertise with

BigQuery ,

Cloud Storage ,

Pub/Sub , and orchestrating workflows with

Composer or Vertex Pipelines.
Data Architecture & Modelling:

Ability to translate diverse business requirements into scalable data models and architect a data lake/lakehouse environment on

GCP .
Engineering Best Practices:

Proficiency with

Git, CI/CD

for data systems, and robust testing methodologies.

Highly desirable skills include:

Domain Experience:

Familiarity with the unique structure of sports data (e.g., event, tracking, scouting, video).
API Development:

Experience building data-centric

APIs , especially with

FastAPI

on serverless platforms like

Google App Engine .
Streaming Data:

Practical experience building real-time data pipelines.
DevOps & MLOps:

Knowledge of Infrastructure as Code

(Terraform), MLOps

principles, and containerization ( Docker, Kubernetes ).

What They Offer

Work that impacts elite football performance and club-wide success
Access to real-world sports data and performance analytics
Flexible working options (hybrid/remote depending on role)
Opportunity to grow with a digital-first team inside a world-renowned club