We're building the data platform that powers decision-making across PRYPCO. As a Software Engineer (Data) , you'll design and operate the systems that make data reliable, accessible, and easy to work with for every team that depends on it.
You'll own problems end-to-end, from architecture to automation, and partner closely with the people who use what you build.
Key Responsibilities
- Design, build, and maintain efficient, reliable data platforms, streamlining end-to-end data pipelines and automating manual workflows.
- Partner with cross-functional teams (Product, Engineering, and Data Science) to translate ambiguous requirements into practical, scalable solutions.
- Establish and enforce data standards, maintain comprehensive documentation, and manage the company-wide data registry.
- Onboard and support platform users while communicating updates and insights through dashboards, bots, and other internal tools.
- Plan and execute organisation-wide platform improvements, defining best practices for coding, testing, deployment, and maintenance.
- Use data to guide engineering decisions and continuously improve platform performance and reliability.
Requirements
- Bachelor's or Master's degree in Computer Science or a related field, or equivalent practical experience.
- Strong proficiency in Python, SQL, and Unix shell scripting .
- Hands-on experience with Agile engineering practices, including TDD, refactoring, CI/CD, and XP .
- Proven experience designing, implementing, and maintaining custom ETL pipelines.
- Experience with workflow orchestration tools such as Apache Airflow .
- Expertise in distributed data processing and query engines, including tools such as:
- Trino
- Apache Spark
- Snowflake
- BigQuery
Nice to Have
- Experience building large-scale infrastructure applications and writing maintainable code across multiple programming languages.
- Expertise with cloud platforms (AWS and/or GCP ).
- Experience with containerisation and Infrastructure as Code, including:
- Docker
- Kubernetes
- Terraform
- Understanding of modern data architecture, with hands‑on experience applying Data Mesh principles.
- Familiarity with notebook-based data science workflows.
- Experience with monitoring and observability tools such as:
- New Relic
- Grafana
- Prometheus
- ELK Stack
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