Financial Data Engineer
Summary
Builds and maintains automated pipelines to extract, normalize, and validate financial data from SEC EDGAR XBRL filings for modeling and analytics using Python.
We are seeking a Financial Data Engineer to support the development of an in‑house financial data extraction and modeling pipeline. This role focuses on programmatic extraction, normalization, and validation of financial data, with a strong emphasis on SEC EDGAR XBRL filings and automated, agent‑driven workflows.
Requirements
- Design and maintain in‑house systems for extracting financial data from regulatory sources
- Parse and normalize SEC EDGAR XBRL filings (10‑K, 10‑Q, 8‑K) into structured, model‑ready datasets
- Work with XBRL components, including taxonomies, contexts, units, dimensions, and company‑specific extensions
- Build automated workflows for data ingestion, validation, reconciliation, and retry handling
- Ensure consistency of financial data across periods, filings, and reporting entities
- Support downstream financial modeling and analytics use cases
- Strong understanding of XBRL data structures and EDGAR filing architecture (required)
- Practical experience in extracting and processing financial data from SEC EDGAR
- Solid knowledge of financial statements and reporting concepts
- Proficiency in Python for data extraction, transformation, and automation
- Experience with libraries such as lxml, BeautifulSoup, pandas, requests, or similar tools
- Experience building reliable and maintainable automation workflows
Preferred Qualifications:
- Exposure to agentic or LLM‑based workflows for document processing or data validation
- Familiarity with additional financial or regulatory data sources
- Experience with data modeling or analytical pipelines
Key Focus: This role requires a practical understanding of how financial data is structured, reported, and extracted at the source level, particularly within XBRL‑based regulatory filings.