We're modernising our data platform and looking for an experienced Data Engineer to help build it.
You'll work with analysts, data scientists and business teams to build reliable pipelines, develop well-structured data models and make data easier to use across the business.
We're moving from legacy systems to a cloud-native stack using Estuary, dbt, Google Cloud Platform (GCP) and Snowflake. There's plenty to work through, and you'll have a say in the architecture, tools and engineering practices we put in place.
If you enjoy getting into the detail, solving problems and seeing your work put to use, we'd like to hear from you.
What you'll work on
Design and build scalable ELT pipelines that bring together data from multiple sources.
Develop and maintain data models using dbt.
Optimise Snowflake models and workloads for performance and cost.
Turn raw data into reliable datasets for reporting and advanced analytics.
Build in data quality checks, monitoring and alerts so issues are picked up early.
Help plan and deliver the migration from legacy systems to the new platform.
Apply sound engineering practices, including testing, CI/CD and code reviews.
Work with technical and business teams to understand what they need and deliver practical solutions.
Contribute to documentation and engineering standards, and support junior engineers as they develop.
Requirements
What you'll need
A relevant tertiary qualification: a diploma or bachelor's degree in Computer Science, Information Technology, Information Systems, Data Engineering or a closely related field.
At least four years' experience in data engineering or a similar role.
Strong SQL skills, including experience with complex transformations and query optimisation.
Proficiency in Python or a similar language for data processing and automation.
Experience building ELT pipelines in a cloud environment such as AWS, Azure or GCP.
Hands-on experience with a modern data warehouse such as Snowflake or Azure Synapse.
Experience using data transformation or orchestration tools such as dbt, Airflow or Dagster.
Experience working with structured and semi-structured data, including JSON and Parquet.
A sound understanding of data governance, security and data quality practices.
You should be comfortable taking responsibility for your work, explaining technical decisions and working through problems with people across the business.
Experience that would be useful
Estuary or other data integration and change data capture tools.
Streaming technologies such as Kafka or Spark Streaming.
BI tools such as Qlik, Power BI, Looker or Tableau.
Qlik Replicate, Qlik Compose or similar tools.
Agile delivery using Jira or Azure DevOps.
DevOps practices or relevant cloud, Snowflake or dbt certifications.
What makes this role interesting
You'll join a growing data function at a point where your experience can help shape how the platform develops. Alongside building pipelines and models, you'll contribute to architecture decisions, establish working practices and mentor junior engineers.
It's an opportunity to work with a modern stack and see the effect of your work across the business.