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- Tasks: Design and build data pipelines for financial datasets, ensuring quality and reliability.
- Company: Join a leading trading and research firm focused on high-quality data solutions.
- Benefits: Enjoy a hybrid work model with 4 days in the office and 1 remote day.
- Why this job: Gain autonomy while tackling impactful engineering challenges in a technical team.
- Qualifications: Proficient in a statically typed language; experience with distributed systems and data quality.
- Other info: All applications are handled discreetly; your privacy is our priority.
The predicted salary is between 43200 - 72000 £ per year.
London – Hybrid (4 days office / 1 remote)
Quant Capital is hiring for a trading and research firm seeking a Software Engineer to join its Data Platform team. This team owns the infrastructure that powers structured, high-quality datasets used across trading, research, and operations. The focus is not on breadth but depth, consolidating the most critical financial datasets into clean, accessible, and reliable pipelines. You’ll work at the intersection of systems engineering, data quality, and automation, shaping the way the firm accesses and relies on structured data.
Role Overview
You’ll design and build robust data ingestion pipelines, apply algorithmic solutions to data validation problems, and contribute to a centralised platform that internal teams use to consume market and reference data. Your work will directly support decisions made across the business daily. This is a high-autonomy role where proactive ownership, clean code, and system-level thinking are valued more than process or oversight.
Key Responsibilities
- Design and maintain pipelines for ingesting and cleaning financial datasets
- Integrate with distributed internal systems to serve structured data to end-users
- Develop new algorithmic techniques for quality control, error correction, and anomaly detection
- Engage with internal stakeholders to shape schema, resolve issues, and anticipate data needs
- Optimise systems for performance, reliability, and long-term maintainability
Required Experience
- Confident programmer in a statically typed language (e.g. Go, Java, C++)
- Strong background in distributed systems or backend platform engineering
- Solid understanding of statistics, data quality metrics, and applied ML concepts
- Experience with relational databases (Postgres, MySQL, etc.) from both usage and administration perspectives
- Ability to analyse noisy data and design tooling to surface problems early
Why Apply?
- Build and own the firm’s centralised, business-critical data platform
- Work on clean, structured engineering challenges with long-term value
- Blend software engineering with data science and system design
- High autonomy, low overhead, and real exposure across trading and research
- Join a deeply technical team working on some of the most important datasets in the business
All applications are handled with strict discretion – nothing is shared without your approval.
Software Engineer – Data Platform Engineering employer: Quant Capital
Contact Detail:
Quant Capital Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer – Data Platform Engineering
✨Tip Number 1
Familiarise yourself with the specific programming languages mentioned in the job description, such as Go, Java, or C++. Brush up on your coding skills and work on personal projects that showcase your proficiency in these languages, especially focusing on data ingestion and processing.
✨Tip Number 2
Gain a solid understanding of distributed systems and backend engineering principles. Consider contributing to open-source projects or building your own applications that involve complex data handling and system integration to demonstrate your expertise.
✨Tip Number 3
Engage with the data community by attending meetups or webinars focused on data quality metrics and machine learning concepts. Networking with professionals in the field can provide insights into industry standards and practices that are highly relevant to the role.
✨Tip Number 4
Prepare to discuss your experience with relational databases like Postgres or MySQL during interviews. Be ready to share examples of how you've used these databases in past projects, particularly in terms of administration and data analysis.
We think you need these skills to ace Software Engineer – Data Platform Engineering
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in software engineering, particularly with statically typed languages like Go, Java, or C++. Emphasise any work you've done with distributed systems and backend platform engineering.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your skills in data quality metrics and applied machine learning can contribute to the Data Platform team. Be specific about your past experiences that align with the job responsibilities.
Showcase Relevant Projects: If you have worked on projects involving data ingestion pipelines or algorithmic solutions for data validation, make sure to include these in your application. Provide links to your GitHub or portfolio if applicable, demonstrating your coding skills and problem-solving abilities.
Highlight Soft Skills: Since this role values proactive ownership and system-level thinking, mention instances where you've taken initiative or led projects. Highlight your ability to engage with stakeholders and resolve issues, as these are crucial for optimising systems and anticipating data needs.
How to prepare for a job interview at Quant Capital
✨Showcase Your Technical Skills
Be prepared to discuss your experience with statically typed languages like Go, Java, or C++. Highlight specific projects where you've designed and built data ingestion pipelines or worked with distributed systems.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your ability to tackle data validation problems. Prepare examples of how you've applied algorithmic solutions in past roles, particularly in quality control and error correction.
✨Understand the Business Context
Research the firm’s trading and research operations. Be ready to discuss how your work can directly impact decision-making across the business, especially in relation to structured data and its accessibility.
✨Engage with Stakeholders
Prepare to talk about your experience working with internal teams to shape data schemas and resolve issues. Show that you can anticipate data needs and communicate effectively with non-technical stakeholders.
