The Penn Wharton Budget Model (PWBM) needs to build and maintain data infrastructure to support its research and analysis of policy effects on budgetary outcomes and economic variables. This includes automating forecasting processes and ensuring data is versioned, accurate, and consistent for internal and external collaborators.
Requirements
- More than 2 years of post-degree experience as a data engineer in a team setting, and familiarity with current cloud data storage options, data modeling, data extraction (e.g., web scraping or using web APIs), automation (e.g., pipelining), and alerting/monitoring with process insights in mind.
- Demonstrated ability to write readable, modular, and testable code.
- Experience with modern software engineering best practices (e.g. automated testing, pair programming, and continuous integration).
- Solid ability to understand mathematical and statistical models.
- Experience working with LLM-based coding tools.
- Experience with data frame packages/libraries in any programming language.
- Knowledge of Python and pandas.
Responsibilities
- design, build, and maintain data infrastructure that houses data obtained from external sources and PWBM model output and enables PWBM and external collaborators easy access to information that is versioned, accurate, and consistent.
- Establish best practices for data ingestion, storage, and retrieval.
- Work with PWBM domain experts to implement best practices in extract-transform-load (ETL) code, including producing readable, modular, and testable code.
- Help the Director of Engineering and Data and domain experts plan future infrastructure development.
- Contribute their expertise and insights to the model automation project to help make the automation pipeline robust to data changes.
Other
- A bachelor's degree in a relevant field, such as computer science, software engineering, statistics, mathematics, or engineering; and 3-5 years of experience; or equivalent.
- Strong written and verbal communication skills and the ability to collaborate successfully with teammates who have diverse technical backgrounds.
- The data engineer will work with individuals across a range of technical experience levels, so the candidate should be able to distill their thoughts into non-technical terms.
- Solid ability to identify requirements from discussions with domain experts and parse them into smaller stories/tasks.
- Position contingent upon funding.
