Data Quality Analyst, Philippines

Job Description: Data Quality Analyst

Location: Remote – Philippines

Employment Type: Full-Time

Work Schedule: 9:00 AM – 5:00 PM Pacific Time

Role Overview:

We are looking for a detail-oriented and analytical Data Quality Analyst to join the VinAudit team at AutoScale Ventures.

VinAudit works with large-scale automotive datasets that support vehicle history, market data, specifications, and other automotive data products. In this role, you will help ensure that the information we provide is accurate, complete, consistent, and reliable.

You will work with large datasets, investigate data quality issues, validate outputs, research discrepancies, and collaborate with our development and quality teams to identify and resolve problems.

This role is well suited for someone who enjoys working with data, investigating inconsistencies, finding patterns, and digging deeper to understand why something does not look right.

What You'll Do:

• Validate automotive datasets to ensure accuracy, completeness, consistency, and reliability.
• Analyze large datasets to identify missing, incorrect, inconsistent, or unexpected data.
• Investigate reported data quality issues and determine their potential source or cause.
• Research external data sources to validate information and support data enrichment.
• Compare data across multiple sources to identify discrepancies and determine expected results.
• Document findings clearly and provide developers with useful information to help reproduce and resolve issues.
• Escalate data-related problems to the appropriate team members and proactively follow up until they are resolved.
• Verify fixes and confirm that identified data issues have been properly addressed.
• Configure and fine-tune data parsing and validation systems as requirements evolve.
• Perform recurring quality checks and help identify patterns that could indicate broader data problems.
• Maintain clear records of investigations, findings, validations, and outstanding issues.
• Work closely with developers and other members of the Quality team to continuously improve the reliability of our automotive data.
• Help improve internal validation processes, checks, and documentation over time.

What We're Looking For:

We are looking for someone who is analytical, detail-oriented, curious, and willing to investigate problems thoroughly rather than simply reporting that something looks incorrect.

You may be a good fit if you:

• Have at least 2 years of professional experience working with data, data analysis, data quality, or a related field.
• Have experience working with and analyzing large datasets.
• Have advanced skills with spreadsheet tools such as Microsoft Excel or Google Sheets.
• Are comfortable comparing, filtering, sorting, and analyzing large amounts of information.
• Have strong attention to detail and can identify inconsistencies that others may overlook.
• Enjoy investigating problems and determining why data may be incorrect.
• Can clearly document your findings and explain issues to both technical and non-technical team members.
• Have strong English reading and writing skills.
• Are organized and able to keep track of multiple investigations and follow-ups.
• Are self-motivated and comfortable working independently in a remote environment.
• Are available to work 9:00 AM – 5:00 PM Pacific Time.

Nice to Have

The following experience is helpful but not required:

• Experience with Python, SQL, or another programming/scripting language.
• Experience with data validation, data cleaning, ETL processes, or data pipelines.
• Familiarity with APIs, JSON, CSV, databases, or other structured data formats.
• Experience investigating technical or data-related bugs.
• Experience with automotive datasets or automotive industry data.
• Previous leadership, management, or supervisory experience.

What Success Looks Like

Success in this role means helping us identify and resolve data issues before they impact our customers.

Over time, we would expect you to:

• Develop a strong understanding of our automotive datasets and how they should behave.
• Independently investigate and validate increasingly complex data quality issues.
• Identify problems proactively rather than relying only on reported issues.
• Provide clear and actionable findings that help developers resolve problems faster.
• Consistently follow outstanding issues through to resolution.
• Help improve our validation processes so recurring problems can be detected early.
• Become someone the team can rely on for accurate and thorough data analysis.<