Senior Data Engineer

As a Senior Data Engineer at GoFundMe, your primary responsibilities include developing and maintaining our enterprise data warehouse, creating and orchestrating ELT pipelines, and integrating data from various systems to ensure that robust analytics and data science projects can thrive. You will also monitor and optimize Snowflake usage, ensuring efficiency and safety in data management.

To excel as a Senior Data Engineer at GoFundMe, you should have at least 5 years of experience in data engineering, demonstrate proficiency in building data pipelines, and have expertise in Snowflake. Additionally, familiarity with orchestration tools, SQL, and programming languages such as Python or Java is essential for the role.

Yes, the Senior Data Engineer role at GoFundMe is a remote position based in Argentina, allowing for flexible work arrangements while still contributing to meaningful projects that impact communities worldwide.

A Senior Data Engineer at GoFundMe should be well-versed in tools like Snowflake for data warehousing, orchestration tools like Airflow, along with a solid understanding of REST APIs, SQL, and various programming languages such as Python or Java. Familiarity with event tracking tools and DBT is also advantageous.

GoFundMe values its employees and provides a host of support mechanisms, including competitive pay, rich healthcare benefits, a workplace culture focused on well-being, and opportunities for professional development, ensuring a thriving environment for a Senior Data Engineer to succeed.

The Senior Data Engineer's work at GoFundMe is pivotal because it underpins the data-driven decisions that enhance our fundraising capabilities. Your contributions lead to actionable insights that empower individuals and organizations to connect and make a positive impact globally, aligning with our mission to help others.

At GoFundMe, learning and development are key priorities. Senior Data Engineers have access to various professional growth programs, mentorship opportunities, and resources aimed at enhancing technical expertise and career advancement within a supportive environment.

When responding to this question, highlight specific projects where you developed ELT pipelines. Discuss the technologies you used, any challenges you faced, and how you overcame them to ensure data integrity and efficiency.

Share specific instances where you've configured or optimized a Snowflake data warehouse. Emphasize your understanding of Snowflake's architecture and showcase any improvements you've implemented that affected performance and cost-effectiveness.

Discuss the methodologies and tools you use to monitor data quality, such as automated testing or validation rules. Provide examples of how you’ve corrected data discrepancies in previous projects to maintain accuracy.

Detail your experience with orchestration tools like Airflow or Prefect. Explain how you design workflows and any complex tasks you managed using these tools, ensuring to highlight the impact on project efficiency.

Describe a specific project where you faced integration challenges, such as dealing with legacy systems or APIs. Discuss your problem-solving approach and how you successfully merged data from disparate sources.

List the programming languages you are proficient in, such as Python or Java. Give examples of tasks you've accomplished using these languages, especially around data manipulation and analysis.

Explain your process for optimizing SQL queries, focusing on techniques such as indexing, query refactoring, or analyzing execution plans and how these strategies improved performance in your past work scenarios.

Discuss your understanding of data security practices, including encryption, access controls, and compliance with data privacy regulations. Provide examples of how you’ve implemented these in previous positions.

Emphasize the importance of teamwork in your role. Provide examples of collaboration with data scientists and analysts on specific projects and how that collaboration led to successful outcomes.

Share your thoughts on emerging data technologies or trends you are keeping an eye on, explaining why you think they will impact the industry and how you envision incorporating them into your future work.