Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will transform high-velocity data into clear financial insights that support trading strategies. You will build and maintain automated PnL reporting frameworks using real-time data streams, analyze trading activity, optimize ClickHouse queries, define KPIs, monitor data quality, automate analysis with Python, and design data models for high-frequency reporting.
Requirements
- University degree in Finance Economics Computer Science or a related quantitative field
- Experience in the finance or fintech industry
- Understanding of financial instruments or trading data
- Advanced SQL knowledge
- Experience with OLAP databases such as ClickHouse
- Strong Python programming skills for data manipulation and automation
- Experience with data visualization tools
- Ability to communicate technical findings to technical and non-technical stakeholders
- Experience with Airflow or similar orchestration tools dbt or Git-based workflows is desirable
- Experience with BI tools such as Superset Metabase Power BI or Grafana is desirable
Responsibilities
- Develop and maintain automated PnL reporting frameworks from real-time data streams
- Conduct trading analytics to identify execution patterns slippage and market impact
- Write and optimize complex SQL queries in ClickHouse
- Define and implement KPIs for trading desks
- Monitor data quality and integrity across data pipelines
- Build Python scripts to automate analytical tasks
- Design data models and schemas for high-frequency reporting
Benefits
- Performance-based bonuses
- Health and wellness benefits
- Flexible working arrangements