Role: Data Quality Engineering & Operations Manager
Location: Remote
Duration: 12 Months Contract to hire
Job description:
We are seeking a Data Quality Engineering and Operations Manager to lead the design, delivery, and operation of enterprise data quality capabilities across operational systems, analytics platforms, and Al pipelines. This role sits within the Data Governance function and owns data quality as a product ensuring data is accurate, complete, timely, and trusted wherever it is used. You will own the data quality roadmap and backlog, manage a team of Data Quality Engineers, and leverage Monte Carlo as a core data observability and quality monitoring platform to detect, prioritize, and resolve data quality issues at scale.
What You wilI Do Product Ownership & Strategy:
Own the enterprise data quality strategy, roadmap, and backlog aligned to data governance objectives and business priorities.
Define success metrics for data quality, including coverage, incident reduction, SLA performance, analytics trust. and Al impact through well-documented and enforceable policy, standard and procedures.
Drive adoption and value realization of data quality policy and standards from Monte Carlo, ensuring it is used consistently and effectively across domains Delivery & Operations.
Translate business, governance, analytics, and Al requirements into actionable data quality rules, thresholds, and monitoring.
Configure and operationalize Morte Carlo to monitor data freshness, volume, distribution, schema changes, and anomalies.
Ensure date quality controls are implemented across: o Source and operational datasets o Curated analytics and semantic layers o Al training, feature, and inference pipelines.
Own day-to-day data quality operations, including alert triage, root cause analysis, and remediation coordination.
Data Quality for Operations & Analytics
Establish and operationalize data quality standards for:
Critical data elements (CEs) used in decision-making
Management and regulatory reporting datasets
Enterprise metrics, KPIs, and dashboards
Use Monte Carlo observability signals to proactively identify upstream issues impacting reports and analytics Improve trust and adoption of analytics through transparent quality metrics and reporting Data Quality for Al & Advanced Analytics
Establish and operationalize data quality standards for Al and ML use cases, including:
Training and validation data completeness and representativeness
Label accuracy and consistency
Schema, volume, and distribution drift detection
Bias, outlier, and feature stability monitoring
Partner with data science teams to identify Al-critical datasets and features
Use Monte Carlo monitoring and anomaly detection to identify data issues that could impact model performance or reliability
People & Stakeholder Leadership
Manage and mentor Data Quality Engineers responsible for rule development, monitoring, and issue analysis.
Collaborate with Data Engineering, Analytics, Data Science, Privacy and Business Data owners.
Communicate data quality health, trends and risks to governance and executive stakeholders.
Required skills:
7+ years of experience in data, analytics, or data management roles with a strong focus on data quality
3+ years in a people-lead role supporting data or analytics platforms
Hands-on experience implementing or operating Monte Carlo or similar data observability platforms
Strong understanding of data quality dimensions across operational, analytical, and Al use cases
Experience working with modern data platforms (cloud data warehouses/lake houses, ETL/ELT pipelines, Bl tools)
Nice to have:
Experience working within a Formal Data Governance organization.
Familiarity with data observability, anomaly detection, and data drift concepts
Experience supporting Al/ML or advanced analytics use cases
Background in regulated industries
Mandatory skills:
Data quality rules, data quality management, data observability, Monte carlo, data quality engineering, data governance and data quality.
Why This Role
Own and evolve an enterprise data quality capability powered by Monte Carlo
Influence how data is trusted across reporting, analytics, and Al
Operate within a mature Data Governance function with executive sponsorship Combine product ownership, platform leadership, and team management
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