How to apply for Senior Data Engineer

About Donorbox

Donorbox is a bootstrapped, profitable fundraising platform that has powered over 100,000 nonprofits to raise more than $4 billion since 2014. As a fully distributed team of 150 people across 23+ countries, it offers the rare combination of startup agility, financial stability, and a mission-driven culture. Named a Best Place to Work in Washington, DC in 2026 and rated #1 on G2 for fundraising software, Donorbox is a place where data directly fuels social impact.

About the role

As a Senior Data Engineer at Donorbox, you will own the design, build, and optimization of ETL/ELT pipelines using SQL, Python, dbt, and Airflow to power analytics across the organization. Your work will directly enable data-driven decisions that help nonprofits raise more funds and manage donors more effectively. This is a high-impact role where you'll collaborate with cross-functional teams to ensure reliable, scalable data infrastructure.

A typical day

You'll start your day with a stand-up with the data team, then dive into building or debugging an Airflow DAG that ingests donation data from multiple sources. After lunch, you might optimize a dbt model to improve query performance for the analytics team, and later collaborate with a product manager to define data requirements for a new feature. Expect a mix of hands-on coding, troubleshooting, and cross-functional communication in a fully remote setting.

Who Donorbox is looking for

• 5+ years of experience in data engineering, with deep expertise in SQL, Python, dbt, and Airflow for building and maintaining production data pipelines.
• Proven track record of designing and optimizing ETL/ELT workflows that handle large-scale, diverse data sources in a cloud environment (e.g., AWS, GCP, or Snowflake).
• Strong understanding of data modeling, warehousing concepts, and performance tuning to support analytics and business intelligence.
• Self-motivated and collaborative, thriving in a fully remote, distributed team environment with a passion for Donorbox's mission to accelerate positive impact.

Tips for this application

• Highlight specific projects where you used dbt and Airflow to build or optimize pipelines—quantify improvements in data freshness, cost, or reliability.
• Showcase your experience with nonprofit or mission-driven data, or explain why Donorbox's mission resonates with you personally.
• Demonstrate familiarity with Donorbox's product by mentioning how data engineering could enhance fundraising insights for nonprofits.
• Emphasize your ability to work autonomously in a remote setting, providing examples of successful remote collaboration and communication.
• Include links to your GitHub or portfolio showcasing data pipeline code (especially dbt and Airflow DAGs) to stand out.

What to cover in your cover letter

["Your hands-on experience with dbt and Airflow, including specific pipeline architectures you've built and the impact they had.", "How you've used data to drive business decisions, ideally in a subscription or payments context similar to Donorbox's fundraising platform.", "Your alignment with Donorbox's mission and excitement about enabling nonprofits through better data infrastructure.", 'Your approach to remote collaboration and how you stay productive and connected in a distributed team.']

Research before applying

• Explore Donorbox's product features, especially their donor management and fundraising tools, to understand the data they generate.
• Read Donorbox's engineering blog or tech stack pages to learn about their data infrastructure and tools.
• Look into recent nonprofit fundraising trends and how data analytics can improve donor engagement and retention.
• Check out Donorbox's G2 reviews and Built In profile to understand company culture and employee satisfaction.

Likely interview topics

Based on the job description, expect questions about:

• Walk us through a complex ETL pipeline you built with dbt and Airflow—what challenges did you face and how did you solve them?
• How do you ensure data quality and reliability in your pipelines? Describe your testing and monitoring strategies.
• Explain how you would design a data model to support donor retention analytics for a nonprofit fundraising platform.
• Describe a time you optimized a slow-performing SQL query or pipeline. What was the impact?
• How do you collaborate with analytics and business teams to understand data requirements and deliver solutions?

Common mistakes to avoid

• Focusing only on technical skills without connecting your work to Donorbox's mission or the nonprofit sector.
• Using generic examples that don't highlight dbt, Airflow, or similar modern data stack tools.
• Neglecting to mention remote work experience or how you thrive in a distributed team, as Donorbox is fully remote.