Data Engineer II

MOO set out to shake up the world of print back in 2006. And we’ve come a long way since. Today we’re a 400 + strong team (we’re thinking of getting matching jackets) who create print and digital products for companies of all sizes – from Google and AirBnB to the guy who makes those amazing prints you found at a craft fair. 

Our global HQ is in London, UK, while we also have premises in Dagenham. In the US, you’ll find us in Boston, MA, as well as East Providence, RI and Denver, CO and now in Cape Town South Africa!

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

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

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