Data Quality Analyst

Who we are:

Quavo is the industry leader in fraud and disputes management technology, offering the world’s only E2E cloud-based Disputes as a Service solution for issuing financial services organizations. Quavo is an organization of tenacious experts who are innovative and challenge complacency to continuously deliver cutting edge technology and AI. Our experts are passionate about delivering value in our solutions and empowering our client community to enhance and evolve industry standards in fraud and disputes management.

About the role:

The Data Quality Analyst ensures the data that powers Quavo's dispute management platform and client reporting is accurate, complete and trustworthy. Working alongside the Data Engineering team, this role defines and monitors data quality rules across our Snowflake data platform, investigates anomalies at their source, and drives fixes with engineering and product teams. The analyst is the first line of defense between upstream system changes and the metrics our financial institution clients, internal teams and leadership rely on. Their work directly reduces reporting errors, protects client trust and supports our compliance and audit commitments.

This role is not sponsorship eligible. Applicants must be legally authorized to work in the United States without employer visa sponsorship, now or in the future.

Responsibilities include:

• Define, implement and maintain data quality checks (completeness, accuracy, validity, timeliness, uniqueness) across ingestion pipelines, dynamic tables and reporting layers in Snowflake.
• Monitor data observability alerts (freshness, volume, schema change, distribution drift), triage incidents, and route root causes to the owning team with clear evidence.
• Investigate data discrepancies reported by clients, Customer Success and internal stakeholders; trace issues upstream through lineage to the originating system or transformation.
• Reconcile key metrics and record counts between source systems, the data warehouse and client-facing reports and data shares.
• Maintain business metric definitions and data documentation in partnership with metric owners, keeping the metric library and data dictionary current.
• Validate new pipelines, schema changes and client onboarding data before release, including multi-tenant isolation and access-control checks.
• Build and maintain data quality dashboards and scorecards that track issue volume, time to resolution and rule coverage.
• Uphold data handling standards for sensitive and regulated data (PII, PCI), supporting SOC audit evidence requests and data governance controls.
• Document recurring issues and recommend preventive fixes to pipelines, source system integrations and data contracts.

Required Qualifications:

• 3-5 years of experience in data quality, data analysis, data validation or a related data role.

• Strong SQL skills, including joins, window functions, aggregations and writing queries to profile and reconcile large datasets.

• Hands-on experience with a cloud data warehouse, preferably Snowflake.

• Experience investigating data issues end to end: reproducing the problem, isolating the root cause and documenting the fix.

• Working knowledge of Python (or a similar scripting language) for data checks and automation.

• Familiarity with data quality dimensions, data profiling techniques and testing approaches for ETL/ELT pipelines.

• Experience with a BI or reporting tool (e.g., Omni, Tableau, Power BI, Looker).

• Comfort working with semi-structured data (JSON) and multiple source systems.

• Clear written communication: able to explain a data issue and its business impact to technical and non-technical audiences.

• Experience working in Jira and Confluence, or similar ticketing and documentation tools.

Preferred Qualifications:

• Bachelor's degree in Information Systems, Computer Science, Statistics, Mathematics or a related field, or equivalent experience.

• Industry certifications: SnowPro Core Certification or another data platform, analytics or data management certification (e.g., CDMP) is a plus.

• Experience with data observability tools (e.g., Metaplane, Monte Carlo, Datadog) or testing frameworks (e.g., dbt tests, Great Expectations).

• Familiarity with Pega (data model, case types or integrations), where upstream application and engineering changes originate.

• Background in financial services, payments, card disputes or fraud.

• Familiarity with regulated data environments (PCI DSS, SOC 1/SOC 2) and row-level security or multi-tenant data models.