At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.
Job Description
The Data Quality Operations Manager is accountable for the operational execution of the Data Quality Framework. While adhering to defined data policies, this role shifts focus to the technical and tactical maintenance of data health. He/she maintains and operationalizes data quality dashboards, applies data engineering best practices to implement robust checks (including anomaly detection), and ensures the proactive monitoring of data issues. This role acts as the bridge between governance policy and technical implementation, ensuring that critical data quality issues are detected, reported, and resolved before they impact business KPIs.
INTERNAL CLIENTS
- Data Engineering & Operations Teams
- Reporting and Analytics Teams
- Specific Business Unit's CDA Analysts
- Data Governance Council
EXTERNAL CLIENTS
- Business Units (Stakeholders relying on KPI accuracy)
- External Data Vendors (if applicable)
Role Summary
The Data Quality Operations Manager is accountable for the operational execution of the Data Quality Framework. While adhering to defined data policies, this role shifts focus to the technical and tactical maintenance of data health. He/she maintains and operationalizes data quality dashboards, applies data engineering best practices to implement robust checks (including anomaly detection), and ensures the proactive monitoring of data issues. This role acts as the bridge between governance policy and technical implementation, ensuring that critical data quality issues are detected, reported, and resolved before they impact business KPIs.
Duties And Responsibilities
- Process (Operations & Engineering)
- Maintain and operationalize data quality dashboards: Oversee the overall presentation of the Data Quality Index, ensuring stakeholders have a real-time view of data health.
- Enhance and operationalize data quality checks: Lead the technical implementation of DQ checks, ensuring they are robust, scalable, and integrated into the data pipeline.
- Apply best practices for data engineering: Utilize engineering standards (CI/CD, version control) when implementing DQ checks and introduce advanced methods such as automated anomaly detection.
- Ensure accuracy of implementation: Validate that implemented checks are correctly configured to enable proper detection of critical issues (minimizing false positives/negatives).
- Proactive Detection & Monitoring: Establish a 24/7 monitoring framework to ensure proactive detection of data quality issues rather than reactive fixes.
- Data Governance alignment: Establish procedures for data security and compliance within the operations, ensuring checks align with the broader Data Governance policies.
- Business (Impact & Reporting)
- Report and cascade data quality issues: Clearly communicate detected issues to stakeholders, specifically highlighting the effect on business KPIs being monitored.
- Root Cause Analysis & Resolution: Collaborate with operational segments and Data Engineering to identify root causes of errors and drive swift resolution.
- SLA Management: Define and manage SLAs for issue resolution depending on the significance of the impact on customers, revenue, and reporting.
- Strategic Alignment: Align data quality operations with strategic business objectives by prioritizing checks on data assets that feed Core KPIs.
- People (Leadership)
- Project Management: Manage and drive teams to run initiatives designed to resolve complex data quality issues.
- Stakeholder Management: Manage expectations on DQ initiatives and follow through on benefit realizations (e.g., improved trust in data).
KPIs
- Time-to-Detect (TTD): Speed at which critical data issues are identified.
- Data Quality Index (DQI) Score: Maintenance and improvement of the overall DQI.
- Coverage: Percentage of critical data elements with active, automated DQ checks.
- Accuracy of Checks: Reduction in false-positive alerts.
TOP 3-5 DELIVERABLES
Operational Data Quality Dashboards: A live view of the organization's data health.
Automated Data Quality Check Suite: A robust library of SQL/DBT-based checks including anomaly detection.
Incident Impact Reports: Documentation linking DQ issues to specific Business KPI impacts.
Data Issue Resolution Framework: Operationalized workflow for fixing detected errors.
Skills
Soft:
- Problem Solving & Root Cause Analysis
- Stakeholder Management & Communication
- Project Management (Agile/Scrum)
- Analytical Thinking
- Attention to Detail
Hard
- Advanced SQL Proficiency (Required)
- Data Engineering Best Practices (CI/CD, Pipeline Orchestration)
- Snowflake (Preferred)
- DBT (Data Build Tool) (Preferred)
- Data Visualization (Looker/Tableu/PowerBI) for Dashboarding
- Python (Scripting for automation is a plus)
Certification/License
- Certifications in Snowflake, Data Engineering are helpful.
COMPETENCIES
Core
- Data Analysis
- Data Quality
- Anomaly Detection & Pattern Recognition
- Data Governance
- Data Profiling & Cleansing
- Data Lineage & Taxonomy
Core/Support
- Data Engineering & Architecture
- Big Data Management
- Project Management
Equal Opportunity Employer
Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here
Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.
