Senior Data Engineer

Summary

Join RYZ Labs as a Senior Data Engineer and build and scale data infrastructure. You will own data pipelines end-to-end, collaborate with cross-functional teams, and contribute to analytics and ML workflows. Design, build, and maintain reliable and scalable data pipelines. Lead the development of new data infrastructure projects. Collaborate with data scientists and product teams to ensure high data quality. Optimize performance and cost across data workflows. Establish and maintain robust monitoring and observability across the data stack. This is a high-impact role in a fast-paced startup environment.

Requirements

• 5+ years of professional experience in data engineering or a related field
• Advanced SQL skills and deep experience with modern data warehouses, especially Snowflake
• Proficient in orchestration tools such as Dagster or Airflow
• Experience with dbt for data modeling and transformation
• Strong Python programming skills
• Experience working in cloud environments, particularly AWS (e.g., S3, Lambda, ECS)
• Familiarity with version control, CI/CD workflows, and Infrastructure-as-Code

Responsibilities

• Design, build, and maintain reliable and scalable data pipelines from raw ingestion to downstream analytics layers (including dashboards and ML features)
• Lead the development of new data infrastructure projects from scratch and own delivery through production
• Collaborate with data scientists, ML engineers, and product teams to ensure high data quality and alignment across stakeholders
• Optimize performance and cost across data workflows (e.g., query tuning, storage optimization)
• Establish and maintain robust monitoring and observability across the data stack

Preferred Qualifications

• Prior experience working in a fast-paced startup environment
• Experience owning and delivering greenfield projects independently
• Deep expertise in Snowflake, including performance tuning and cost optimization
• Knowledge of cost optimization strategies for cloud-based data infrastructure
• Background in analytics or ML data preparation
• Comfortable with ambiguity and able to make decisions in lightly structured environments