Pythia Sports
Quant Data Engineer
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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.
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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
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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
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Jessica, London
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