Data Quality Manager Overview: We’re looking for a Data Quality Manager in Bogotá, Colombia, to play a key role in driving excellence in Premise’s Market Performance Operations team. In this role, you will ensure the accuracy, completeness, and timely delivery of high-quality data, working closely with cross-functional teams to improve our processes and data products. Your efforts will directly influence the quality of Premise’s market research studies and contribute to data-driven decision-making across the organization. As a key part of the team, you’ll not only oversee data quality but also drive improvements in how we govern, manage, transform, and deliver data. If you are passionate about data integrity and ready to shape the future of data operations, we want to hear from you. Qualifications: Proven experience in managing data quality, data transformation, and data completeness. Expertise with data pipelines, transformation processes, and integration tools (SQL, Python, Looker, etc.). Experience working with and managing data scientists. Strong problem-solving skills with the ability to identify and resolve complex data issues. Experience with standardizing data schemas and maintaining consistency across data sources. Knowledge of data automation and model development, particularly in large-scale environments. Excellent collaboration skills, with the ability to work across cross-functional teams to drive results. Key Responsibilities: Data Quality Management (DQM) for Commercial Products Develop and document a comprehensive Data Quality Management (DQM) process for commercial products, focusing on ensuring consistency, accuracy, and transparency across data operations. Update the Data Governance approach, including the documentation of data governance rules and ownership. Clearly outline roles and responsibilities across teams to ensure adherence to data quality standards. Establish a transparent ruleset for what data will be improved and what will not, providing clarity to all stakeholders on the scope of improvement efforts based on impact, feasibility, and business priorities. Work with cross-functional teams to ensure alignment on the DQM process and its ongoing execution across the organization. Define and track key Data Quality Metrics to evaluate and report on data quality performance. Establish targets for each metric and create systems for continuous monitoring and improvement. Data Quality Oversight Ensure stable, reliable, and accurate data production across all countries in which MPS is active. Lead efforts to improve data accuracy and reduce delivery timeframes across teams. Develop and manage frameworks for measuring quality escapes and set reduction targets. Ensure on-time delivery of high-quality market research studies. Standardize and optimize quality processes to drive consistent results. Collaborate with operations teams to implement strategies for continuously improving data quality. Provide actionable recommendations for both short- and long-term improvements to data quality. Alert Prioritization & Proactive Investigation Develop and implement a framework to prioritize existing data quality alerts based on severity and impact. Create a system to surface critical alerts that require proactive investigation and immediate resolution. Collaborate with Customer Success, Data Delivery, and Operations teams to define key metrics for evaluating alert severity. Drive continuous improvement in alert handling processes to ensure high-priority issues are addressed promptly. Root Cause Analysis of Data Anomalies Develop a framework to conduct root cause analysis to identify the underlying drivers of data quality issues. Collaborate with cross-functional teams to pinpoint systemic issues and design solutions to prevent recurrence. Develop and implement corrective actions based on insights from root cause analysis to improve data production processes. Identify, understand and resolve anomalies in data content, such as unexpected spikes in availability or changes in core census variables. Develop predictive models to identify and manage anomalies proactively. Data Loss Reduction Identify and resolve issues related to data loss, incomplete submissions, and inaccuracies to ensure data completeness. Conduct deep dives into data transformation issues and provide actionable solutions for data transformation pipelines. Work with Engineering to resolve inconsistencies and anomalies in data transformation pipelines. Automated Quality Checks (AQC) Drive the implementation of AQC check suggestions, collaborating with Product teams to integrate controls at data collection points. Manage automated fraud detection models and ensure prompt identification of potential issues. Propose and implement additional AQC checks based on identified data quality challenges. Segmentation and Trend Models Oversee segmentation and trend models, developing predictive tools to identify data anomalies and drive continuous improvements. Why Join Us? Lead impactful initiatives that shape the future of our data ecosystem. Collaborate with talented, cross-functional teams. Continuous growth opportunities within a dynamic company environment. If you are ready to make an impact and help drive high-quality data operations, we encourage you to apply and join our talented team. #J-18808-Ljbffr