Principal Data Engineer

Summary

Join Ryz Labs as a Principal Data Engineer to design, build, and own modern data platforms end-to-end. You will leverage your expertise in data engineering and software development to create scalable and resilient data products. This role demands proficiency in Python, SQL, Spark/Databricks, and cloud infrastructure (AWS, Azure, or GCP). You will collaborate with cross-functional teams, champion data quality, and implement robust monitoring and optimization strategies. The ideal candidate thrives in fast-paced environments and values collaboration and craftsmanship. Ryz Labs offers a remote and distributed work environment with opportunities for growth and development.

Requirements

• Databricks certified data engineers
• Cloud specific data engineering certifications
• Python mastery – OOP, type hints, packaging, and performance tuning
• Spark mastery - Performance tuning and data engineering optimizations
• SQL mastery - Expert SQL and data-model design for relational databases
• Infrastructure-as-Code (Terraform, CDK, or ARM) and CI/CD tooling (GitHub Actions, Jenkins, Azure DevOps)
• Knowledge of observability/monitoring (Datadog, Prometheus, OpenTelemetry) for data services
• Well-versed in the setup of data quality and data integrity pipelines
• Comfortable with writing design documents

Responsibilities

• Design & build pipelines – Develop, test, and maintain robust batch and streaming data pipelines in Python, SQL, and Spark/Databricks
• Engineer infrastructure – Provision and evolve cloud resources (AWS, Azure, or GCP) via IaC frameworks such as Terraform or CloudFormation
• Apply software-craft disciplines – Enforce version control, automated testing, CI/CD, and code reviews; write clean, idiomatic, well-documented Python
• Author design artefacts – Produce clear architectural diagrams, ADRs, and technical design documents that guide implementation and onboarding
• Monitor & optimize – Instrument pipelines, build dashboards, and tune performance; own incident response and root-cause analysis
• Champion data quality & governance – Implement validation, lineage, and observability to guarantee trustworthy data assets
• Collaborate cross-functionally – Partner with product, data science, and platform teams to translate business questions into scalable data solutions