Data Platform Engineer — ML & Analytics Infra

Visa Hunt; New York (NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible PTO

Home-office equipment

Health and wellness stipend

Intra-office travel budget

Weekly coffee stipend

Job summary

Ramp is building the data-centric infrastructure that powers analytics and machine learning across the company. This role focuses on platforms supporting the data science lifecycle, partnering with applied scientists, AI engineers, risk engineers, and other ML developers to accelerate model development.

You will build and scale components of Ramp's Analytics and ML platforms, collaborating with stakeholders to productionize ML applications, while ensuring reliability and cost efficiency across

Qualifications

• Experience with workflow orchestration tools (Airflow, Dagster, Prefect).
• Experience building infrastructure on AWS, GCP, or Azure.
• Knowledge of SQL and Snowflake/Redshift/BigQuery or similar databases.
• Intuition around analytics and ML workflows; strong Python skills.

Responsibilities

• Build and integrate components of Ramp's Analytics Platform and ML Platform.
• Develop tools to improve data experience for scientists and engineers.
• Collaborate with stakeholders to productionize ML applications.
• Build reliable, scalable, maintainable, and cost-efficient systems across the stack.

Skills

Workflow orchestration tools (Airflow,

Dagster

Prefect

Cloud data infra (AWS/GCP/Azure)

SQL & major warehouses (Snowflake/Redp

Tools

Terraform

Datadog

Job description

Ramp is building the data-centric infrastructure that powers analytics and machine learning across the company. This role focuses on platforms supporting the data science lifecycle, partnering with applied scientists, AI engineers, risk engineers, and other ML developers to accelerate model development.

You will build and scale components of Ramp's Analytics and ML platforms, collaborating with stakeholders to productionize ML applications, while ensuring reliability and cost efficiency across