Data Quality Engineer: III

Job Title: Data Quality EngineerFully RemoteLocation: Nearshore - LATAMRole Summary: The Data Quality Engineer is responsible for building, operating, and improving automated data quality and observability controls across operational systems, analytics platforms, and AI data pipelines. This role focuses on preventing, detecting, and diagnosing data issues before they impact reporting, decision-making, or AI/ML models.The Data Quality Engineer uses Monte Carlo as a core observability platform and applies strong SQL, analytical, and engineering skills to implement scalable monitoring, anomaly detection, and issue resolution workflows across the modern data stack.Key ResponsibilitiesData Quality & Observability Engineering• Design, implement, and maintain automated data quality and observability monitors using Monte Carlo• Configure monitoring for:o Data freshness, volume, and schema changeso Distribution shifts and statistical anomalieso Upstream/downstream impact using lineage• Embed data quality checks into ingestion, transformation, and analytics pipelinesData Quality for Operations & Analytics• Implement quality controls for operational and analytical datasets, including:o Critical data elements (CDEs)o Curated analytics and semantic layerso Reporting, metrics, and KPI datasets• Investigate data quality alerts and perform root cause analysis using lineage and pipeline context• Partner with analytics and engineering teams to remediate issues and prevent recurrenceData Quality for AI & Advanced Analytics• Apply data observability to AI and ML pipelines, including:o Monitoring training and inference data for drift and anomalieso Validating feature stability and data consistency• Support early detection of data issues that could impact model performance, reliability, or explainability• Collaborate with data science teams to monitor AI-critical datasets and featuresIssue Management & Continuous Improvement• Triage, prioritize, and resolve data quality incidents using defined workflows and SLAs• Document root causes, fixes, and preventive controls• Continuously improve monitoring coverage, alert quality, and signal-to-noise ratioCollaboration & Governance Enablement• Partner with Data Governance, Analytics, and Business Data Stewards to align technical controls to data quality standards• Support audit, risk, and compliance needs by providing evidence of monitoring and issue resolution• Contribute to reusable patterns, templates, and best practices for data quality engineeringRequired Qualifications• 4+ years of experience in data engineering, analytics engineering, or data quality roles• Strong SQL skills and experience working with large, complex datasets• Hands-on experience implementing or operating Monte Carlo or similar data observability platforms• Experience working with modern data stacks (cloud data warehouses/lakehouses, ELT pipelines, BI tools)• Strong analytical skills and ability to perform root cause analysis across data pipelinesPreferred Qualifications• Experience supporting data quality for AI/ML or advanced analytics use cases• Familiarity with data lineage, data catalogs, or governance platforms• Experience in regulated or high-risk data environments (financial services, healthcare, etc.)• Exposure to CI/CD or automated testing patterns for data pipelinesKey Skills & Competencies• Data observability and anomaly detection• SQL and data analysis• Root cause analysis and problem solving• Collaboration across engineering, analytics, and governance teams• Automation mindset with attention to signal quality• Clear technical communication