Trading Data Analyst - Risk & Client Analytics
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Company Description
Finalto is a global leader in liquidity provision and trading technology solutions, serving institutional and B2B clients across financial markets worldwide. With regulated entities in the UK, Singapore, Cyprus, Australia, and the UAE, and additional operational teams in Denmark and Bulgaria, we combine international reach with local expertise.
Our business spans multi-asset liquidity, risk management, and cutting-edge trading platforms, supporting
clients in achieving more efficient and sustainable growth. At the core of our success is our commitment to
innovation, operational excellence, and robust governance.
As part of a global financial services group, Finalto combines the scale and stability of an established
organisation with the agility of a fintech innovator. We are driven by collaboration, integrity, and performance.
Joining Finalto means becoming part of a diverse and dynamic team where your contributions have real
impact. We invest in our people, offering opportunities for professional growth, international exposure, and the chance to shape the future of trading technology and liquidity solutions.
Role Description
We are looking for a Trading Data Analyst – Risk & Client Analytics to join our Risk team in London.
This role sits at the intersection of trading, risk, data, and analytics. The successful candidate will analyse trading activity, client behaviour, execution quality, market data, and risk exposure to support informed decision-making across Risk, Trading, Quant, Data, and Technology teams.
This is not a pure reporting role. The role requires an investigative mindset and the ability to work with complex trading datasets to identify patterns, explain client flow, and provide practical insights that support decisions around client segmentation, pricing, execution setup, liquidity management, hedging strategy, and risk appetite.
Key Responsibilities
- Analyse client trading behaviour, profitability, execution quality, order-flow characteristics, market impact, and risk exposure.
- Work with large trading and market datasets, including trade data, tick data, pricing data, execution data, order flow, and client-level performance data.
- Identify meaningful trends, anomalies, behavioural patterns, risk concentrations, and changes in client or product performance.
- Develop analytical approaches to better understand client flow, trading patterns, execution outcomes, and market-data behaviour.
- Support business decisions relating to risk appetite, client segmentation, pricing, execution configuration, liquidity management, and hedging strategy.
- Build and maintain dashboards, reports, and analytical tools that improve visibility of trading activity, execution performance, and trading risk.
- Use Python, SQL, Databricks, and other data tools to extract, clean, transform, analyse, and visualise data.
- Support automation of recurring analysis, monitoring, and reporting processes.
- Collaborate with Data and Technology teams to improve data quality, pipelines, and analytical frameworks.
- Communicate findings clearly to both technical and non-technical stakeholders.
What We Are Looking For
- Experience in data analysis, trading analytics, risk analytics, execution analytics, market data, or another data-intensive analytical environment.
- Strong Python and SQL skills.
- Familiarity with Databricks or similar data analytics platforms.
- Strong analytical and problem-solving skills, with excellent attention to detail.
- Ability to work with large, complex, and sometimes imperfect datasets.
- Good understanding of statistics, data visualisation, and performance analysis.
- Strong interest in financial markets, trading behaviour, execution quality, market microstructure, and client-flow analysis.
Nice to Have
- Experience with KDB/q, tick data, quote data, order lifecycle data, or high-frequency market data.
- Exposure to client-flow analysis, execution analytics, trading-pattern analysis, or market-data analytics.
- Familiarity with Java, C++, Streamlit, BI tools, dashboarding, or internal analytics applications.
- Exposure to FX, CFDs, equities, futures, commodities, indices, crypto, or other traded products.
- Understanding of trading concepts such as spread, slippage, liquidity, latency, order flow, execution quality, client profitability, hedging, and risk exposure.
- Previous experience in a broker, liquidity provider, market maker, hedge fund, bank, trading firm, e-trading business, or fintech environment.
Preferred Background
- Degree in Computer Science, Mathematics, Statistics, Engineering, Quantitative Finance, Data Science, or a related quantitative discipline.
- Relevant professional experience, ideally around 2–5 years, although we are open to candidates with equivalent experience and strong capability.