Senior Hybrid ML & Data Engineer: Production-Ready Pipelines

Correlate Resources; Sydney; Hybrid; AUD 150,000 - 210,000; Full time

Job summary

Correlate Resources in Sydney is seeking a hands-on Senior Hybrid Machine Learning and Data Engineer to work across data engineering and ML, turning raw policy, claims, operational and document data into reliable datasets and production-ready solutions.

You will own end-to-end lifecycle from ingestion to monitoring, collaborating across data, ML and platform teams to build scalable, observable pipelines and robust models deployed in production.

Qualifications

• Hands-on experience across data engineering and machine learning
• Strong Python and SQL skills with production experience
• Experience building data pipelines for structured and unstructured data
• Experience taking models from development to production
• Understanding of model training, validation and monitoring
• Familiarity with cloud-based data/ML platforms
• Ability to build reliable, observable pipelines for production
• Experience with large, complex datasets
• Strong communication of technical decisions

Responsibilities

• Build and maintain scalable data ingestion and transformation pipelines.
• Work with structured, semi-structured and unstructured data.
• Create usable, model-ready datasets from hard-to-access information.
• Implement data quality and validation controls.
• Develop reusable feature engineering workflows using Python and SQL.
• Support production deployment, monitoring and retraining of models.

Skills

ML & Data Eng

Python

SQL

Data pipelines

Production ML

Model training

Cloud platforms

Unstructured data

Communication

Observability

Job description

Correlate Resources in Sydney is seeking a hands-on Senior Hybrid Machine Learning and Data Engineer to work across data engineering and ML, turning raw policy, claims, operational and document data into reliable datasets and production-ready solutions.

You will own end-to-end lifecycle from ingestion to monitoring, collaborating across data, ML and platform teams to build scalable, observable pipelines and robust models deployed in production.