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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

KPIs

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.
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.
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.
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: Adoption of the standardized, reusable DQ check frameworks and patterns the role defines.

Top 3-5 Deliverables

Reusable DQ Engineering Standards & Frameworks: Codified best practices, reusable macros and patterns, and CI/CD test scaffolding adopted across the team.
Operational Data Quality Dashboards: The technical pipeline and presentation behind a live view of the organization's data health.
Technical Root-Cause & Incident Analysis: Documentation linking DQ issues to specific Business KPI impacts and to their source-level technical fixes.
Anomaly Detection & Monitoring Implementation: The deployed statistical / ML detection and proactive monitoring tooling.
Automated Data Quality Check Suite: A robust library of SQL / dbt-based checks, including statistical and ML-based anomaly detection.

Requirements

SKILLS

Soft

Problem Solving & Root Cause Analysis
Stakeholder Communication & Technical Influence
Technical Solution & Workstream Leadership (Agile / Scrum)
Analytical Thinking
Attention to Detail

Hard

Expert SQL Proficiency (Required)
Data Engineering Best Practices — CI/CD, version control, pipeline orchestration (Required)
Python for automation and statistical methods (strong proficiency)
Snowflake (strongly preferred)
DBT (Data Build Tool) (strongly preferred)
Statistical & ML-based Anomaly Detection methods (preferred)
Data Visualization (Looker / Tableau / Power BI) for dashboarding

Certification / License

Certifications in Snowflake, dbt, or Data Engineering are helpful.

Work Experience

5+ years of experience working with data management, data engineering, or data quality teams.
5+ years of hands-on experience building and implementing enterprise-scale data warehouse or data quality monitoring solutions.
3+ years as a senior or lead technical individual contributor, owning the design and delivery of complex data solutions and providing technical leadership of workstreams (technical leadership rather than people management).
Experience with Data Governance tools and methodologies.

Level of Knowledge

Expert SQL proficiency is mandatory.
Strong, hands-on familiarity with Snowflake and DBT is highly preferred.
Deep understanding of data engineering best practices in implementing checks (idempotency, modularity, testing, CI/CD).
Working knowledge of statistical and ML-based anomaly detection methods, with the ability to implement and tune them.
Ability to understand key business drivers and how data quality affects business KPIs.
Deep understanding of data profiling, cleansing, and metadata management.

Education

Bachelor's Degree in Computer Science, Computer Engineering, Information Systems, Statistics, or a related degree.

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

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