As a Software Engineer on the Data Platform team at Ramp, you'll be responsible for building and integrating components of our Analytics and Machine Learning Platforms. Your role will involve creating tools to support Ramp's Data Scientists, Analytics Engineers, and Operations teams, developing essential batch and streaming data pipelines, and collaborating with various teams to produce analytical products and machine-learning systems.

To excel as a Software Engineer at Ramp, you should have experience with workflow orchestrators like Airflow or Dagster, and have built data infrastructure on cloud services such as AWS or GCP. A strong understanding of SQL, experience with databases like Snowflake or Redshift, and solid Python programming skills are essential. A track record of creating reliable data infrastructure will be highly regarded.

As a Software Engineer in the Data Platform team at Ramp, you'll work with a variety of exciting technologies including Airflow for orchestrating workflows, Snowflake and ClickHouse for data storage, and Kafka for stream processing. You'll also gain experience with machine learning systems to make data-driven decisions that propel Ramp forward.

Ramp fosters a collaborative and innovative culture for Software Engineers, encouraging team members to share ideas and drive improvements. With a mix of seasoned professionals from top tech and finance companies, the environment is dynamic and inspiring, offering plenty of opportunities to learn and grow within your role while making impactful contributions.

Ramp offers a competitive benefits package for its Software Engineers, including 100% medical, dental, and vision insurance coverage, a flexible PTO policy, wellness stipends, and a 401k plan with employer matching. Additionally, Ramp supports work-life balance with a WFH stipend and parental leave, ensuring that employees have everything they need to thrive both professionally and personally.

When answering this question, provide concrete examples of how you've utilized Airflow in previous roles. Discuss specific workflows or data pipelines you've built, any challenges faced, and how you overcame them. This helps demonstrate your practical knowledge and problem-solving skills.

In your response, discuss best practices for building robust data infrastructure. Mention the importance of monitoring, automated testing, and performance optimization. You might also share specific tools you've used to achieve these goals, along with any metrics that demonstrate your success.

Highlight your systematic approach to troubleshooting, including steps like logging, tracking down errors, and isolating problems to identify root causes. Providing an example of a specific issue you've resolved can showcase your analytical thinking and technical skills.

Use the STAR (Situation, Task, Action, Result) method to structure your answer. Detail the context, what you needed to achieve, the actions you took to overcome obstacles, and the successful outcomes. This approach clearly illustrates your problem-solving abilities and contributions.

Discuss your communication style and collaborative processes. Mention specific experiences where effective teamwork led to successful project outcomes. Emphasizing your flexibility and willingness to learn from other experts can highlight your teamwork skills.

Provide specific examples of how you've leveraged cloud services for data storage or processing. Discuss challenges you've faced and how cloud solutions have improved your project's efficiency. Displaying familiarity with different services shows your breadth of expertise.

Share any relevant projects where you've integrated machine learning models into your data pipelines or infrastructure. Discuss the processes involved and the measurable impacts these implementations had on decision-making and operations.

Express your understanding of data observability and its importance in maintaining healthy data systems. Discuss tools or practices you've implemented to improve observability, showcasing your proactive approach to data management.

Mention resources you rely on, such as blogs, online courses, webinars, or professional groups. Highlight any recent trends or technologies you've incorporated into your work, showing your commitment to continuous learning.

Talk about specific experiences working with both batch and streaming data processes. Describe the technologies you used and the trade-offs you considered, emphasizing your ability to choose the right approach for varying data needs.