- Understanding of temporal data modelling, including the handling of
historical state and change over time.
- Knowledge of schema design, schema evolution and data compatibility
considerations.
- Understanding of partitioning, clustering and other techniques used to
improve data performance at scale.
- Ability to make sensible design choices across normalized and denormalized
models, and between natural and surrogate keys.
- Practical approach to data quality, reconciliation and root-cause analysis.
- Experience building or supporting production data pipelines in a
collaborative engineering environment.
- Experience working with distributed data processing frameworks such as Apache
Spark.
- Working knowledge of common data formats such as JSON, Avro and Parquet.
- Stronger ownership of technical design across multiple datasets or pipeline
domains.
- Experience guiding implementation standards, code quality and engineering
practices within a team.
- Ability to lead delivery for a workstream, manage dependencies and support
less experienced engineers.
Technology Environment
The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.