ONE OF THE FASTEST-GROWING FINTECHS IS HIRING! JOIN THE ANNA MONEY TEAM: REVOLUTIONISING BUSINESS ADMINISTRATION FOR AUSTRALIAN SMES.

At ANNA Money, we’re more than just a mobile app and business current account—we’re transforming the way small businesses handle banking, tax, and financial admin. As a Top 25 Startup (Bloomberg, 2024) and one of the fastest-growing fintechs in Europe, we are now bringing our innovative financial services to Australia.
Our mission is simple: eliminate the hassle of tax, invoicing, and admin so that business owners can focus on their passions. By combining cutting-edge AI, automation, and intuitive financial tools, we make business administration effortless. With over 120,000 customers and a rapidly expanding product suite, ANNA is at the forefront of fintech innovation.
Now, we’re looking for a Data Engineer to join our Sydney team and help build the data infrastructure behind our new deposit-holding account (ANNAOne) and our rapidly growing business credit card product.

KEY SKILLS & EXPERIENCE

• 5+ years of experience in data engineering, analytics engineering, or data architecture.
• Proficiency in SQL & Python (experience with Pandas, NumPy, or Spark is a plus).
• Experience with modern data platforms, such as Google Cloud (BigQuery, AlloyDB, CloudSQL) or AWS (Redshift, S3, Glue).
• Strong understanding of ETL/ELT pipelines, real-time streaming (Kafka, Pub/Sub, RabbitMQ), and data orchestration (Airflow, dbt, Dagster).
• Familiarity with financial data – understanding banking APIs, credit risk modelling, or Open Banking data is a big plus.
• Knowledge of data governance, security, and compliance best practices in financial services.
• Hands-on experience with CI/CD pipelines, Kubernetes, and workflow automation tools.

NICE-TO-HAVE SKILLS

• Experience with financial transaction data, Open Banking APIs, or credit risk analytics.
• Knowledge of ATO tax compliance frameworks and regulatory reporting.
• Exposure to machine learning engineering (MLflow, Vertex AI, or TensorFlow Serving).
• Experience with real-time fraud detection and anomaly detection.

• Design and Build Data Pipelines – Develop scalable ETL/ELT pipelines to process real-time financial and transactional data.
• Support Banking & Credit Products – Create robust data architectures that power both deposit accounts and credit decisioning.
• Enhance Fraud Detection & Credit Risk Models – Work closely with data scientists and risk teams to refine credit scoring models, transaction monitoring, and fraud prevention.
• Optimize Data Processing & Storage – Build high-performance data solutions using Google Cloud (BigQuery, AlloyDB, PostgreSQL, Kafka, etc.).
• Enable Data-Driven Decision Making – Develop self-service analytics tools for product, finance, and risk teams.
• Ensure Compliance & Security – Implement best practices for data governance, security, and regulatory compliance in fintech.
• Deploy ML Models into Production – Partner with data scientists to operationalize machine learning models for automated insights.