Financial Data QA & Validation Analyst

Hiretop; United States; Remote; USD 65,000 - 95,000; Full time

Job summary

Hiretop is seeking a detail-oriented Data Analyst/Data QA to join a technology-focused team. The role centers on validating extraction results, analyzing data quality, and identifying patterns in failures to improve processes.

You will review large volumes of financial data and collaborate with Python developers on extraction logic. Ideal candidates have 4+ years in data analysis/QA, strong analytical skills, and experience with financial datasets.

Qualifications

• Must have 4+ years as Data Analyst, DataQA or Data Quality Specialist.
• Strong analytical thinking and problem-solving skills.
• Ability to analyze large data volumes and identify patterns.
• Experience defining validation criteria and test cases.
• Strong attention to detail and accuracy.
• Experience with financial data or structured datasets.
• Experience reviewing large document sets or extracted data.
• Ability to work with developers to improve data quality.
• Comfortable running and analyzing data tests and validation processes.

Responsibilities

• Review and analyze large volumes of extracted financial data.
• Identify patterns in failed or incorrect data extraction.
• Investigate root causes of data inconsistencies and validation failures.
• Work with thousands of documents with varying formats and quality.
• Define and document clear validation criteria and test cases.
• Collaborate with Python developers to improve extraction logic.
• Run and monitor data validation and testing processes.
• Analyze failed datasets and provide actionable insights to improve data accuracy.
• Ensure high standards of data quality and consistency.

Skills

Data analytics

Data QA

Data quality

Attention to detail

Problem solving

Python

Tools

Python

Job description

Hiretop is seeking a detail-oriented Data Analyst/Data QA to join a technology-focused team. The role centers on validating extraction results, analyzing data quality, and identifying patterns in failures to improve processes.

You will review large volumes of financial data and collaborate with Python developers on extraction logic. Ideal candidates have 4+ years in data analysis/QA, strong analytical skills, and experience with financial datasets.