Data Engineer (Financial Analytics focus)
On-site · Melbourne, Victoria, Australia
Job Summary
Design and develop reporting pipelines and datasets aligned with business requirements while implementing enhancements to existing reporting processes for accuracy and usability. Responsibilities include automating data delivery processes, identifying and implementing automated data quality checks, and resolving data quality issues. Collaborate with business stakeholders to gather requirements and ensure outputs meet expectations. Required qualifications include 4+ years in analytics engineering, advanced PostgreSQL skills, a strong understanding of data warehouse architecture, and hands-on experience with Apache Airflow. Proficiency with Git and JIRA, along with experience in designing data warehouse architecture and building ELT/ETL pipelines is essential.
Required Qualifications
- 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
Desired Qualifications
- 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
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