• Designing, building, and maintaining efficient and reliable data platform, streamlining end-to-end processes and automating workflows
• Partnering with cross-functional teams (product, engineering, data science, 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

• Bachelor's or Master's degree in Computer Science or a related field, or equivalent practical experience
• Proficiency in Python, SQL, and Unix Shell scripting
• Demonstrated experience in custom ETL design, implementation, and maintenance, along with workflow orchestration using tools like Airflow
• Proven experience building large-scale infrastructure applications and writing maintainable code in multiple programming languages
• 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 (New Relic, Grafana, Prometheus, ELK)

• Experience implementing agile software development best practices, including TDD, refactoring, CI/CD, and XP
• Extensive experience working with distributed data processing and query engines (e.g., Trino, Spark, Snowflake, Big Query)
• Expertise in cloud (GCP, AWS), containerisation, and infrastructure-as-code (Docker, Kubernetes, Terraform)

Experience building data platforms using Spark, Trino, Presto, Flink, or similar, with a focus on data quality, SQL performance tuning, and data warehousing principles