Data Engineer (Financial Analytics focus)
Summary
Build and maintain financial data pipelines and reporting systems using PostgreSQL, Airflow, and ELT/ETL processes for a global trading platform.
We are a leading trading platform that is ambitiously expanding to the four corners of the globe. Our top-rated products have won prestigious industry awards for their cutting-edge technology and seamless client experience. We deliver only the best, so we are always in search of the best people to join our ever-growing talented team.
We are seeking a Data Engineer to join our Financial Analytics team developing and maintaining data pipelines, transformation logic, and data quality checks in a complex multi-jurisdiction financial data platform (PostgreSQL DWH + Airflow orchestration).
We are a leading trading platform that is ambitiously expanding to the four corners of the globe. Our top-rated products have won prestigious industry awards for their cutting-edge technology and seamless client experience. We deliver only the best, so we are always in search of the best people to join our ever-growing talented team.
We are seeking a Data Engineer to join our Financial Analytics team developing and maintaining data pipelines, transformation logic, and data quality checks in a complex multi-jurisdiction financial data platform (PostgreSQL DWH + Airflow orchestration).
Responsibilities
- 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 of data delivery processes
- Identify and implementation of automated data quality checks
- Resolve of issues related to data quality
- Collaborate with business stakeholders to gather reporting requirements, clarify logic, and ensure outputs meet expectations
Requirements
- 4+ years in analytics engineering or similar data-focused roles
- Advanced PostgreSQL: 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
- Experience designing and evolving data warehouse architecture and data models
- Track record of building robust, maintainable ELT/ETL pipelines in production
- Experience implementing automated data quality checks
- Domain fluency in financial and trading concepts, with the 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
- Hands-on experience with dbt, particularly in the context of migration or adoption initiatives
- Exposure to Snowflake or strong interest in working with it as part of a target data architecture
- Proficiency in Python for scripting, automation, and data pipeline tooling
- Background in fintech or financial services in any capacity