Senior Data Engineer (Data Foundations)

This role emphasizes observability, automation, and optimization, cleaning up unused tables, streamlining data pipelines, and ensuring data engineers and analysts can move quickly with confidence. You'll also play a key role in supporting the lifecycle of ML and GenAI data workflows. You'll report to the Engineering Manager of the team.

What You’ll Do:

• Design, build, and enhance pipelines that make the data ecosystem more reliable, observable, and efficient.
• Implement monitoring and observability frameworks to ensure pipelines are performant and resilient.
• Work with APIs to build integrations, handle errors (HTTP status codes, retries), and enforce secure credential management.
• Identify and clean up unused or redundant data tables to streamline system performance.
• Participate in code reviews, team rituals, and contribute to best practices in pipeline development.
• Collaborate with non-technical stakeholders to understand data requirements and deliver actionable solutions.
• Support live products via on-call rotations, ensuring uptime and reliability.

Availability: Full-time.

What’s in it for you?

1. Learn and evolve your skills using the latest and greatest technology tools in a rapidly growing company, including new AI tooling rolling out this quarter.
2. Learn from the best people around you. We constantly challenge the status quo and invent new ways of building a great product.
3. A laid-back office with a fully stocked kitchen, lunch provided, game nights, and regular team activities.
4. Work on challenging problems, innovate, and positively impact many people's lives while having fun doing it.

Required Qualifications

• Advanced to fluent speaking and writing English.
• Bachelor's degree in Computer Science or related field (or equivalent work experience).
• 5+ years of experience with Python and SQL for data engineering.
• Strong hands-on experience with DBT and Airflow (must have).
• Experience with data warehousing and distributed data ecosystems (Databricks, Snowflake, or similar).
• Solid understanding of observability practices (logging, metrics, tracing) for data pipelines.
• Familiarity with DevOps practices (CI/CD, GitHub Actions, Infrastructure as Code).
• Experience building secure and resilient API integrations.
• Comfortable supporting production systems through on-call rotations.

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