Data Engineer
12 hours ago
Data Engineer – Databricks / AWS (Mid–Senior Level) Sydney | Hybrid Work | Fintech Scale-Up Environment
Are you a Data Engineer who loves building scalable data pipelines and enabling real-time machine learning systems?
Join a fast-growing tech company that's transforming how Australians interact with data-driven products — using cutting-edge cloud and ML infrastructure to deliver seamless digital experiences.
You'll be part of a high-impact Data & ML Engineering team responsible for the platforms, pipelines, and frameworks that power real-time insights and intelligent customer experiences.
What You'll Do
- Design, build, and maintain data pipelines and ETL frameworks in Databricks and AWS
- Support real-time inference and model deployment pipelines for low-latency ML systems
- Collaborate with Data Scientists and ML Engineers to productionise models and improve reliability
- Implement best practices in data quality, testing, observability, and version control
- Contribute to evolving the data platform architecture and engineering standards
What We're Looking For
- 3–6+ years' experience in Data Engineering or similar roles
- Strong hands-on experience with Databricks (Spark, Delta Lake, dbt or similar)
- Solid background with AWS data stack (S3, Glue, Lambda, Redshift, Kinesis, etc.)
- Skilled in Python and SQL for data transformation and automation
- Understanding of MLOps concepts (model deployment, monitoring, automation)
- Experience or exposure to real-time / online inference pipelines and streaming data
- Strong engineering principles — CI/CD, version control, testing, performance optimisation
- Bonus: experience with MLflow, Airflow, or Terraform
Why You'll Love It Here
- Be part of a modern data culture where your work directly supports ML-driven products in production
- Work alongside a collaborative, high-performing team passionate about data and impact
- Hybrid work flexibility (Sydney-based)
- Competitive salary + potential equity
- Real opportunity for career growth as the team scales
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