Quant Data Engineer

Role Overview

• 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
• 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
• 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

Requirements

• 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

Tech Stack

• Amazon Redshift
• BigQuery
• Postgres
• Python
• SQL

Benefits

• Private health and dental insurance
• Cycle to work scheme
• Enhanced parental leave
• Enhanced sick pay
• Increased holiday allowance
• Plenty of career development opportunities