Software Engineer (Data)
Summary
Build and maintain data pipelines and platforms in Python/SQL to make data reliable and accessible for teams, using tools like Airflow, Spark, and Snowflake.
Software Engineer – Data
We’re building the data platform that powers decision‑making across PRYPCO. As a Software Engineer Data, you will design and operate systems that make data reliable, accessible, and easy to work with for every team that depends on it. You will own problems end‑to‑end—from architecture to automation—and partner closely with the people who use the solutions 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 organization‑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, and 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 containerization and Infrastructure‑as‑Code, including Docker, Kubernetes, and 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, and ELK Stack.