Data Quality Engineering & Operations Manager

Randstad Digital Americas is offering a contract opportunity for a Data Quality Engineering & Operations Manager. This role involves leading the design, delivery, and operation of enterprise data quality capabilities, ensuring data is accurate, complete, timely, and trusted across various systems and platforms.

Responsibilities:

• 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 AI 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
• Translate business, governance, analytics, and AI requirements into actionable data quality rules, thresholds, and monitoring
• Configure and operationalize Monte Carlo to monitor data freshness, volume, distribution, schema changes, and anomalies
• Ensure data quality controls are implemented across:
• Source and operational datasets
• Curated analytics and semantic layers
• AI training, feature, and inference pipelines
• Own day-to-day data quality operations, including alert triage, root cause analysis, and remediation coordination
• Establish and operationalize data quality standards for:
• Critical data elements (CDEs) 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
• Establish and operationalize data quality standards for AI 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 AI-critical datasets and features
• Use Monte Carlo monitoring and anomaly detection to identify data issues that could impact model performance or reliability
• 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

Requirements:

• 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 AI use cases
• Experience working with modern data platforms (cloud data warehouses/lakehouses, ETL/ELT pipelines, BI tools)
• Experience working within a formal Data Governance organization
• Familiarity with data observability, anomaly detection, and data drift concepts
• Experience supporting AI/ML or advanced analytics use cases
• Background in regulated industries