Data Quality Lead Expert

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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 Lead Expert is the senior technical authority responsible for the design, engineering, and continuous improvement of the technical implementation of the Data Quality Framework. As a hands-on individual contributor, this role personally engineers the most complex and high-impact data quality checks including statistical and ML-based anomaly detection and defines the engineering standards, reusable patterns, and tooling that the wider data quality team adopts. While adhering to defined data policies, the role concentrates on the technical depth of data health: instrumenting robust, scalable, and idempotent checks, operationalizing the data quality dashboards, and ensuring proactive monitoring of data issues. It serves as the deep technical bridge between governance policy and engineering implementation, ensuring that critical data quality issues are detected, diagnosed, and resolved before they impact business KPIs. The role leads through technical expertise, standard-setting, and influence rather than through direct people management. DUTIES AND RESPONSIBILITIES: 1. Process (Operations & Engineering) Design and build the data quality check suite: Personally engineer the most complex, high-impact DQ checks and the reusable check frameworks behind them, ensuring they are robust, modular, idempotent, and scalable across the data platform. Architect anomaly detection and statistical monitoring: Develop, tune, and operationalize advanced detection methods (statistical and ML-based anomaly detection) and the reusable macros / wrappers that let the team apply them consistently. Own engineering standards and reference patterns: Define and codify data engineering best practices - CI/CD, version control, testing, modularity for DQ implementation, and establish the reference patterns and tooling that others build on. Operationalize data quality dashboards: Build and maintain the technical pipeline behind the Data Quality Index so stakeholders have a real-time, trustworthy view of data health. Ensure accuracy and continuous tuning of checks: Validate that checks are correctly configured and continuously tune them to maximize true detection while minimizing false positives and false negatives. Proactive detection & monitoring: Engineer the monitoring framework alerting, scheduling, and observability that enables continuous, proactive detection of data quality issues rather than reactive fixes. Data governance alignment: Implement the technical controls for data security and compliance so that checks align with the broader Data Governance policies. 2. Business (Impact & Reporting) Diagnose and communicate data quality issues: Provide clear, rigorous technical analysis of detected issues, articulating their effect on the business KPIs being monitored to both stakeholders and engineering peers. Root Cause Analysis & Resolution: Lead deep technical root-cause investigation alongside Data Engineering and drive the engineering fixes that resolve errors at source. Enable SLA adherence: Recommend technically feasible resolution targets and build the detection, triage, and tooling that make timely, SLA-aligned resolution possible. Technical prioritization & strategic alignment: Apply technical judgment to prioritize checks on the data assets that feed Core KPIs, aligning engineering effort with strategic business objectives. 3. Technical Leadership & Influence Technical authority: Act as the go-to expert for data quality engineering, owning the direction and decisions on the hardest technical problems and design trade-offs. Standards & reusable assets: Raise the technical bar across the team through reusable frameworks, codified standards, reference implementations, and thorough technical documentation - an individual-contributor role without direct people-management responsibility. Technical project leadership: Lead the end-to-end technical execution of complex DQ initiatives scoping, design, build, and delivery collaborating across Data Engineering, Analytics, and Governance. Influence & advisory: Influence stakeholders, peers, and the Data Governance Council through technical credibility, advising on feasibility, risk, and the best technical approach for DQ initiatives. KPIs Coverage: Percentage of critical data elements with active, automated DQ checks engineered and delivered by the role. Accuracy of Checks: Reduction in false-positive (and false-negative) alerts through well-designed, well-tuned checks. Time-to-Detect (TTD): Speed at which critical data issues are identified by the checks and monitoring the role builds. Data Quality Index (DQI) Contribution: Technical contribution to maintaining and improving the overall DQI. Engineering Reuse & Standardization: Adopt

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