At a Glance
- Tasks: Join a dynamic team to prepare and enhance data for predictive sports modelling.
- Company: Pythia Sports, a fast-growing tech company in the gaming industry.
- Benefits: Hybrid work model, private health insurance, enhanced leave, and career development opportunities.
- Other info: Enjoy a relaxed atmosphere with regular social events and remarkable colleagues.
- Why this job: Make a real impact by solving complex data challenges in sports modelling.
- Qualifications: Strong Python and SQL skills, experience with complex datasets, and a passion for problem-solving.
The predicted salary is between 60000 - 80000 £ per year.
About us
Pythia Sports is a fast-growing technology company, delivering innovative solutions to the gaming industry since 2014. Our business is all about predictive sports modelling, underpinned by extensive use of a wide and ever-expanding array of real-time feeds and inputs. If that sounds both complex and hard, it is, but it’s also a huge amount of fun! We strive to be the best at what we do every day, and we know that our success comes from our most important resource - our people. We pride ourselves on hiring talented individuals who challenge the status quo and help us to build exceptional, high-performing teams. Based in London Victoria, we’re working to a hybrid work-from-home/office model with 2 days a week in the office. We offer private health and dental insurance, a cycle to work scheme, enhanced parental leave, enhanced sick pay, increased holiday allowance and plenty of career development opportunities. At Pythia, you will find a relaxed atmosphere, regular social events and remarkable colleagues looking to push technology boundaries - come and join us!
The Role
As a Quant Data Engineer, you will be joining a small but growing team working closely with Pythia’s quant modellers to ensure they have access to reliable, well-structured and high-quality data for research, modelling and analysis. With particular emphasis on data quality, usability, investigation and continuous improvement, you will work side-by-side with quant, engineering and operational colleagues to prepare datasets, improve underlying data flows and help ensure that the data feeding our models is accurate, complete and fit for purpose. Your experience in handling complex datasets, building robust Python-based data workflows and investigating data issues will be key as we continue to expand our modelling capabilities and data inputs. From preparing historical datasets to assessing new data sources and improving existing ones, you will play a central role in helping Pythia extract maximum value from its data. Sharing our passion for delivering fantastic solutions, you will leave no stone unturned to help drive Pythia’s success and to be part of getting us to the next level.
3 Best Things About the Job
- Impactful Work: You will work directly with quant modellers on the data that underpins core research, modelling and decision-making.
- Complex Challenges: You will engage with intricate real-time and historical datasets from diverse sources, helping turn messy inputs into reliable modelling assets.
- Pioneering Solutions: You will tackle unique data problems that sit at the heart of predictive sports modelling.
What you will be doing
- Quant Data Support
- Work day-to-day with quant modellers to prepare, refine and maintain datasets used for research, modelling and analysis.
- Help improve the structure, quality and usability of underlying data so that it can be consumed efficiently by quant workflows.
- Investigate data issues affecting modelling outputs, identifying root causes and working with relevant teams to resolve them.
- Support the development of repeatable data preparation processes that make research datasets more reliable, consistent and easier to work with.
- Proactively review existing and new data sources to determine what can be consumed, how it should be processed and where improvements are needed.
- Data Preparation & Engineering
- Build and maintain Python-based data workflows and supporting pipelines for ingestion, transformation and validation of modelling data.
- Maintain and further develop Pythia’s historical data assets, ensuring they remain accurate, accessible and fit for analytical use.
- Work with engineers to improve upstream and downstream data flows, helping ensure that critical data is captured and processed effectively.
- Support data migrations, backfills and structural improvements where required to improve the usefulness and reliability of modelling datasets.
- Contribute to the development of tooling and processes that make it easier to explore, prepare and troubleshoot data used by the quant team.
- Data Quality, Investigation & Improvement
- Ensure data quality and integrity through validation, reconciliation and targeted monitoring across key datasets.
- Expand visibility into data issues by improving checks, alerts and investigative workflows across critical pipelines and sources.
- Define and improve data logic, transformations and assumptions, ensuring they are clearly documented and consistently applied across datasets.
- Improve the clarity and usability of data through better documentation, metadata management and standardisation of definitions.
- Work closely with engineering and operational teams to resolve anomalies, gaps and inconsistencies in source data.
- Contribute to the ongoing evolution of Pythia’s data capabilities, balancing immediate modelling needs with longer-term improvements to data quality and maintainability.
Measures of Success
In the first three months, you will have:
- Fully understood the key datasets, data flows and modelling dependencies across the platform.
- Built strong working relationships with quant modellers and become a trusted partner in preparing and investigating data.
- Contributed to meaningful improvements in at least one modelling dataset, data workflow or data quality process.
What you need for this role
- Strong experience in a Quant Data Engineer, Research Data Engineer or similar role working with complex datasets.
- Strong Python experience for data processing, investigation and workflow development.
- Excellent SQL skills and strong experience working with relational databases, preferably PostgreSQL.
- Proven experience preparing, transforming and validating datasets for analytical, modelling or research use cases.
- Strong experience investigating data issues and tracing problems through pipelines, transformations and source systems.
- Experience building and maintaining data pipelines or processing workflows in production environments.
- A strong understanding of data quality, reconciliation and validation practices.
- Experience working closely with technical stakeholders to understand how data is consumed and how it can be improved.
- Confidence working with messy, incomplete or evolving datasets and turning them into reliable assets for downstream users.
- Experience with analytical data warehouse technologies such as ClickHouse, BigQuery, Snowflake, Redshift or similar would be beneficial.
- Experience with version control systems (preferably GitLab) and working with tools such as JIRA & Confluence.
- Experience working in Agile environments and collaborating with distributed teams.
- Ability to work well in a dynamic, fast-paced environment and quickly adapt to new technologies and requirements.
- A passion for detail and problem solving, with excellent verbal and written communication skills.
Who you are
- Customer-focused: everything we do is with our partners and stakeholders in mind.
- Analytical: you are comfortable digging into complex datasets to identify issues, inconsistencies and opportunities for improvement.
- Curious: you like understanding how data behaves, where it comes from and how it can be made more useful.
- Organised: staying on top of multiple datasets, priorities and investigations will be crucial.
- Thrive under pressure: Pythia Sports is growing quickly, and we work hard, so we want you to enjoy being challenged!
- Relevant: the marketplace, our competitors and our partners move fast, so you need to help us stay ahead by applying best practices in modern data engineering.
- Team Player: building great teams is how we will succeed.
Quant Data Engineer in London employer: Pythia Sports
Pythia Sports is an exceptional employer that fosters a dynamic and innovative work environment in the heart of London Victoria. With a strong emphasis on employee well-being, we offer comprehensive benefits including private health insurance, enhanced parental leave, and ample career development opportunities. Our collaborative culture encourages creativity and teamwork, making it an exciting place for talented individuals to thrive while tackling complex challenges in predictive sports modelling.
StudySmarter Expert Advice🤫
We think this is how you could land Quant Data Engineer in London
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even casual coffee chats. Building relationships can open doors to opportunities that aren’t advertised.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects or contributions, especially those involving Python and data workflows. This will give you an edge and demonstrate your hands-on experience.
✨Ace the Interview
Prepare for technical interviews by brushing up on your SQL and Python skills. Be ready to discuss how you've tackled complex datasets and resolved data issues in the past. Confidence is key!
✨Apply Through Our Website
Make sure to apply directly through our website! It shows you're genuinely interested in joining Pythia Sports and helps us keep track of your application more efficiently.
We think you need these skills to ace Quant Data Engineer in London
Some tips for your application 🫡
Show Your Passion for Data:When writing your application, let us see your enthusiasm for data engineering! Share specific examples of how you've tackled complex datasets and improved data quality in your previous roles. We love candidates who are genuinely excited about the work they do.
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your relevant experience as a Quant Data Engineer. Use keywords from the job description to demonstrate that you understand what we're looking for and how you fit into our team.
Be Clear and Concise:We appreciate straightforward communication, so keep your application clear and to the point. Avoid jargon unless it's necessary, and make sure your achievements stand out. We want to quickly grasp your skills and how they align with our needs.
Apply Through Our Website:Don't forget to submit your application through our website! This helps us keep everything organised and ensures your application gets the attention it deserves. Plus, it shows you're keen on joining our team at Pythia Sports!
How to prepare for a job interview at Pythia Sports
✨Know Your Data Inside Out
Before the interview, dive deep into the types of datasets Pythia Sports works with. Familiarise yourself with predictive sports modelling and think about how your experience aligns with their needs. Being able to discuss specific data challenges you've faced will show that you're ready to tackle the complexities of the role.
✨Show Off Your Python Skills
Since strong Python experience is crucial for this position, be prepared to discuss your past projects involving data workflows. Bring examples of how you've built or maintained data pipelines, and if possible, share any code snippets or tools you've developed. This will demonstrate your hands-on expertise and problem-solving abilities.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about Pythia's data processes, team dynamics, and future projects. This not only shows your genuine interest in the company but also helps you gauge if it's the right fit for you. Think about what you want to learn from the role and how you can contribute.
✨Emphasise Team Collaboration
Pythia values teamwork, so highlight your experiences working closely with quant modellers and engineers. Share examples of how you've collaborated to improve data quality or resolve issues. This will illustrate your ability to thrive in a dynamic environment and your commitment to building strong working relationships.
