Data Engineer - Data Engineering

Technology & Product - Data Engineering

What you’ll do:

• Help evolve our modern data platform to support reliable analytics and emerging AI use cases

• Design, build, and maintain robust, production-grade data pipelines using Dagster, with a strong focus on reliability, observability, and performance

• Implement data quality checks and reliability patterns to ensure trusted, high-quality data across the platform.

• Contribute to the definition and evolution of data modelling standards, dbt best practices, and governance to keep our analytics layer modular, testable, and scalable

• Collaborate with analytics and product teams to enable impactful data and AI use cases

• Identify opportunities to improve the architecture, tooling, and developer experience of the data platform as our needs scale

What we’re looking for

We are looking for someone who brings:

• 3–5 years experience in Data Engineering or a similar role

• Strong Python skills with a focus on writing clean, modular code to enhance and extend platform capabilities

• Experience working with orchestration frameworks (e.g. Dagster, Airflow, Prefect, etc), building dependable, self healing and well structured workflows

• Proven SQL & dbt (or similar tools, e.g. Dataform) skills with a focus on performance, modularity, readability and scalable analytics-focused models

• Experience with modern cloud data warehouses or databases for analytics (we use Snowflake, but others are also welcome)

• Strong focus on data quality, including testing, validation, and proactive issue detection

• Confidence investigating and debugging data issues end-to-end, performing root cause analysis

• Thoughtful views on data modeling and software best practices, with the ability to advocate for standards while remaining open to pragmatic alternatives

Nice to have

If you have any of the below experiences, we would love to hear about it. However, they are absolutely not required. If you are uncertain, reach out to us anyway.

• Near real-time or streaming analytics pipelines

• Supporting data related AI or ML use cases

• Data observability and quality monitoring tools (e.g. Elementary, etc)

• Implementing Metrics/Semantic layer

• Data contracts, schema governance and data classification practices

• Worked with BI Tools (Tableau, Looker, etc)

• Terraform

• Experience with Cloud Services (AWS, Azure, GCP, etc)

• Data Warehouse performance tuning and optimisation