Requirements

A bachelor's or master's degree in computer science or related field, or equivalent practical experience
Proficiency in Python, SQL, and Unix Shell scripting
Experience implementing agile software development best practices, including TDD, refactoring, CI/CD, and XP
Demonstrated experience in custom ETL design, implementation, and maintenance, along with workflow orchestration using tools like Airflow
Extensive experience working with distributed data processing and query engines (e.g., Trino, Spark, Snowflake, BigQuery)
Experience building data platforms using Spark, Trino, Presto, Flink, or similar, with a focus on data quality, SQL performance tuning, and data warehousing principles
Experience building large-scale infrastructure applications and writing maintainable code in multiple programming languages

Nice to have

Expertise in cloud (GCP, AWS), containerisation, and infrastructure-as-code (Docker, Kubernetes, Terraform)
An understanding of modern data architecture with experience implementing data mesh principles
Familiarity with notebook-based data science workflows and proficiency in using monitoring and logging tools (NewRelic, Grafana, Prometheus, ELK)

Designing, building, and maintaining an efficient and reliable data platform, streamlining end-to-end processes and automating workflows
Partnering with cross-functional teams (Product, Engineering, Analytics) to build and enhance a seamless data platform, translating abstract concepts into practical solutions
Establishing and enforcing data standards, maintaining comprehensive documentation, and managing a company-wide data registry
Training and supporting users, and communicating platform updates and insights through various channels (dashboards, bots, etc.)
Planning and executing organisation-wide platform changes, ensuring consistent best practices for coding, testing, deployment, and maintenance
Leveraging data to guide all aspects of engineering work, ensuring insight-driven outcomes