As a Data Engineer at PitchBook Data, your primary responsibility is to build, optimize, and maintain the data pipelines that fuel the PitchBook platform. This involves writing scalable Python scripts to automate the extraction of financial data from thousands of external sources, ensuring that the platform's company profiles, deal histories, and executive contacts remain completely up to date.

You will design database schemas that accurately represent complex financial structures, such as cap tables, fund allocations, and corporate hierarchies. This requires close collaboration with data analysts and research teams to understand their data entry workflows and translate their business requirements into high-performance database models. You will also build automated data validation frameworks to catch inconsistencies, duplicates, or formatting errors before the data is published to the production platform.

Additionally, you will play a key role in modernizing the data infrastructure. This includes migrating legacy batch processes to real-time or event-driven pipelines, optimizing database query performance, and ensuring that the engineering team's data storage practices comply with security and data privacy regulations.

To be competitive for the Data Engineer position, you should possess a strong foundation in software engineering principles applied specifically to data systems.

• Must-have skills – Strong proficiency in Python and SQL. Experience building robust web scrapers or integrating with external REST APIs. Solid understanding of relational database design, normalization, and query optimization.
• Nice-to-have skills – Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker), and workflow orchestration tools (like Apache Airflow). Familiarity with financial datasets or capital markets structure is a distinct advantage.
• Experience level – Typically requires 2+ years of professional experience in a data engineering or backend software engineering role, with a proven track record of delivering production-grade data pipelines.
• Soft skills – Exceptional communication skills, a highly collaborative attitude, and the ability to remain resilient and focused when dealing with ambiguous data sources or fast-moving project timelines.

Q: How difficult is the PitchBook Data interview process? A: The technical difficulty is generally rated as average to easy, but it requires a high degree of practical execution. The focus is less on abstract LeetCode algorithms and much more on real-world engineering skills, such as building functional scrapers, parsing messy data, and designing clean database schemas.

Q: What is the timeline for the entire hiring process? A: The process typically takes between 2 to 4 weeks. This includes a week allocated for the take-home project, followed by scheduling the technical reviews and final manager interviews.

Q: What are they looking for in the take-home project? A: They are looking for production-ready code. This means your project should not only work correctly but should also be modular, well-documented, include error handling (e.g., handling failed network requests), and feature a logical database design.

Q: Where is this position located, and what are the work expectations? A: While PitchBook Data has a major engineering presence in Seattle, WA, they support hybrid work environments depending on the specific team and office location. Be sure to clarify current hybrid and in-office expectations with your recruiter during the initial call.

Write clean, production-grade code for your take-home assignment. Do not treat the take-home project as a quick prototype. Use proper directory structures, write docstrings, handle exceptions gracefully, and include a clear README file explaining how to run your code and the architectural choices you made.

Be prepared for dynamic scheduling and pipeline speeds. Some candidates have reported that the hiring team can move very quickly or face internal scheduling shifts. If your interviewers are running slightly late, remain professional, composed, and ready to make a great impression once the meeting starts.

Show enthusiasm for financial data. You do not need an investment banking background, but showing a genuine interest in venture capital, startups, and how financial markets operate will make your conversations with the hiring manager and teammates much more engaging.

The Data Engineer role at PitchBook Data is an exceptional opportunity for engineers who love working with complex data and want to see their work directly impact high-stakes financial decisions. By building the infrastructure that harvests, cleanses, and structures global market intelligence, you will be contributing directly to the core value proposition of a highly respected industry leader.

To maximize your chances of success, focus your preparation on the practical aspects of data engineering: web scraping, API integration, Python scripting, and SQL database design. Dedicate ample time and care to the take-home project, as it serves as the foundation for your technical evaluation in the subsequent rounds. For more detailed company insights, interview prep materials, and candidate experiences, be sure to explore the resources available on Dataford.

The salary data displayed above represents the typical compensation structure for this engineering level. When evaluating an offer from PitchBook Data, consider the complete package, which often includes a competitive base salary, performance-based bonuses, and comprehensive benefits. Use this data to benchmark your expectations and guide your discussions with the recruiting team.