Data Engineer (Financial Analytics)

We are a leading trading platform that is ambitiously expanding globally. Our top-rated products have won prestigious industry awards for their cutting-edge technology and seamless client experience. We are continuously growing our talented team.

• 4+ years in analytics engineering or similar data-focused roles
• 2+ years in fintech or relevant domains (retail FX/CFD, hedge or quant funds, prop trading firms, or financial services technology)
• Advanced PostgreSQL skills including stored procedures and functions, complex CTEs, window functions, SCD2 patterns, query plan analysis and optimisation
• Strong understanding of data warehouse architecture: staging, core, and data mart layers; incremental load patterns; slowly changing dimensions
• Hands-on experience with Apache Airflow: DAG authoring, scheduling, dependency management, and failure handling
• Proficiency with Git (GitLab or GitHub) and JIRA
• Practical experience implementing complex financial calculation logic in SQL (e.g., P&L, margin, equity, positions, ledger reconciliation)
• Ability to interpret regulatory or business specifications and translate them into precise, testable SQL transformation logic
• Solid domain fluency in financial and trading concepts, with ability to understand requirements and clearly explain implemented logic to business stakeholders
• High degree of autonomy: able to reverse-engineer undocumented systems, identify root causes, and take end-to-end ownership of pipelines and calculation logic
• Comfortable using AI-assisted development tools (e.g., Claude, Copilot, Cursor) to improve productivity

Nice to have:

• Experience with regulatory reporting frameworks: MiFID II, EMIR, FCA, CySEC, ASIC or equivalent
• Domain knowledge in risk management, compliance reporting, internal accounting, spread/commission capture, margin requirements
• Experience with Tableau or equivalent BI tool, specifically with live datasources backed by DWH functions
• Familiarity with dbt
• Exposure or interest in Snowflake

• Implement enhancements and changes to existing reporting processes to improve accuracy, performance, and usability
• Design and develop new reporting pipelines and datasets aligned with business requirements
• Automate data delivery processes
• Identify and implement automated data quality checks
• Resolve issues related to data quality
• Collaborate with business stakeholders to gather reporting requirements, clarify logic, and ensure outputs meet expectations