Senior Data Engineer ( SQL / Databricks / PySpark )
Melbourne / Hybrid
$150-180k base DOE plus super
A Senior Data Engineer with
Databricks
experience is required to join an established and successful retail / ecommerce company based in Melbourne.
This is a hands-on senior role focused on building reliable, scalable, production-grade data assets that support analytics, forecasting, optimisation and AI use cases. Acting as the technical authority for analytics engineering, this role bridges raw data ingestion, curated analytical datasets and ML-ready feature layers, ensuring analytics and data science teams can work with confidence and speed.
Key Responsibilities for the
Senior Data Engineer ( SQL / Databricks / PySpark )
include :
* Build and optimise production-grade Databricks pipelines using PySpark and SQL.
* Develop curated analytical datasets using transformation frameworks to support advanced analytics and machine learning use cases.
* Enable data science teams with feature-ready, ML-friendly datasets.
* Improve pipeline reliability, performance, and cost efficiency.
* Define, uplift, and embed analytics engineering standards, patterns, and best practices.
* Apply strong data modelling, testing, and documentation practices.
* Collaborate with analytics, data science, and governance teams to deliver trusted, reusable data assets.
Essential Experience
* Senior-level, hands-on experience working with modern analytics platforms in production environments.
* Strong capability in distributed data processing and SQL-based analytics.
* Practical experience with analytics engineering tooling and frameworks.
* Experience supporting advanced analytics or machine learning use cases.
* Strong data modelling skills and disciplined engineering practices.
Desirable Experience
* Exposure to model lifecycle management or feature management tooling.
* Experience working within cloud-based data environments.
* Experience optimising performance and cost on large-scale data processing platforms.
What Success Looks Like After 12 Months
* Stable, high-performing, production-grade analytics pipelines operating at scale.
* Advanced analytics and machine learning teams actively consuming reliable, feature-ready datasets.
* Reduced pipeline failures alongside measurable improvements in performance and cost efficiency.
* Clearly defined and embedded analytics engineering standards across the platform.
* Strong senior ownership of analytics engineering and machine-learning data enablement.
Keywords:
SQL / Data Engineering / PySpark / Databricks / Python / Data Engineering / Machine Learning