Lead Data Governance Engineer

Global Payments Inc.

. Assess the quality and fitness for purpose of data used in new and existing products .

Posted 11/4/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data quality assessment, anomaly investigation, and root-cause analysis, with a strong ability to communicate technical data issues to diverse stakeholders. Proficient in establishing data quality controls and collaborating across teams to ensure data assurance throughout the product lifecycle.

Highest-signal resume keywords

Advanced SQL SkillsData Quality AssessmentAnomaly InvestigationRoot-Cause AnalysisData Governance Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalyticsData QualityData EngineeringData ProfilingData ReconciliationData MonitoringData Quality RulesThreshold DefinitionComplex Dataset InvestigationData Contracts

Soft Skills

Strong Written CommunicationStrong Verbal CommunicationIndependent Operation

Tools & Technologies

SnowflakeDatabricksAWS

Industry Keywords

Merchant AcquiringPayment Service ProviderCard Network EnvironmentData-Intensive Products

Tech Stack

Tools & technologies

AWSSnowflakeSQL

About the role

Key responsibilities & impact

Assess the quality and fitness for purpose of data used in new and existing products
Establish acceptance criteria and thresholds based on product requirements and business criticality
Build repeatable profiling, reconciliation and monitoring controls across critical datasets
Monitor fill rates, null rates, volumes, ranges, distributions, schema changes and other material quality measures
Investigate complex data anomalies and discrepancies
Identify root causes across source systems, ingestion, transformation, business logic and downstream consumption
Partner with accountable teams on remediation
Define semantic expectations, data contracts, automated alerts and tolerances for critical product inputs
Document definitions, dependencies, assumptions, limitations and appropriate uses
Communicate data risks and potential product, operational, business and client impact to technical and non-technical stakeholders
Recommend systemic improvements that reduce recurring issues
Collaborate across Product, Data Engineering, Data Science, Data Governance and business subject-matter experts to embed data assurance throughout the product development lifecycle

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Statistics, Mathematics, Information Systems, Engineering or a related field
10+ years of experience in data analytics, data quality, data engineering or a related discipline
Ability to operate independently as a senior individual contributor
Advanced SQL skills
Demonstrated success profiling, reconciling and investigating large, complex datasets
Experience defining data-quality rules, thresholds, controls and monitoring
Experience with anomaly investigation and root-cause analysis
Strong written and verbal communication skills
Ability to translate technical data issues into clear product, business, operational and client implications
Experience in merchant acquiring, a payment service provider or a card network environment is a bonus
Experience supporting data-intensive products is a bonus
Familiarity with Snowflake, Databricks, AWS, data contracts or data-product operating models is a bonus
If working in the United States, must undergo a drug test after a conditional offer

Benefits

Comp & perks

Hybrid work arrangement
Reasonable accommodations during the hiring process and for performing essential job functions
Opportunity to learn from industry experts and grow your career
Dynamic career opportunities and global team environment