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Mid-level metro data analyst with generalist Python and market-data requirements yields high competition.
Requires market-data and financial domain experience, limiting cross-industry transferability.
Explicit 3+ years, mandatory financial-market data experience, and Python/pandas requirements make shortlisting highly strict.
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Develop and maintain Python-based data pipelines to ingest, transform, validate, and load external financial datasets.
Own assigned financial datasets throughout their lifecycle, including monitoring pipelines, resolving data issues, and improving data reliability.
Collaborate with internal teams and external vendors to ensure data quality, address delivery problems, and support documentation for maintainability.
3+ years of experience in Financial Data, Market Data, Data Engineering, Data Operations, or similar roles.
Bachelor's or master's degree in quantitative disciplines such as Mathematics, Physics, or Engineering.
Advanced Python programming skills including use of data libraries like Pandas and NumPy.
Experience working with structured and semi-structured financial data formats (CSV, JSON, XML, Parquet, APIs) and familiarity with Git.
Experienced in handling and reconciling financial market data, reference data, pricing, fundamentals, corporate actions, ESG, or alternative datasets.
Proven ability to identify and resolve data quality issues including missing data, duplicates, outliers, and inconsistencies.
Able to coordinate effectively with global cross-disciplinary teams and external vendors to maintain high data integrity and adapt to changes.