Job Description Premise seeks a skilled professional to play a key role in driving data quality excellence and analytical insights within its Market Performance Operations team. The ideal candidate will have experience in managing data quality, data transformation, and data completeness processes. They will be responsible for ensuring the accuracy, consistency, and timely delivery of high-quality data, working closely with cross-functional teams to improve our processes and data products. Key Responsibilities - Develop and document a comprehensive Data Quality Management process for commercial products. - Update the Data Governance approach, including the documentation of data governance rules and ownership. - Establish a transparent ruleset for what data will be improved and what will not. - Work with cross-functional teams to ensure alignment on the DQM process and its ongoing execution across the organization. Requirements - Proven experience in managing data quality, data transformation, and data completeness processes. - Expertise with data pipelines, ETL processes, and integration tools. - Strong problem-solving skills with the ability to identify and resolve complex data issues. - Experience with standardizing data schemas, maintaining consistency across data sources, and using best practice techniques. - 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. Benefits This role offers the opportunity to make a significant impact on Premise's data quality and operations. The successful candidate will receive a competitive compensation package and a dynamic work environment. What You Will Do In this role, you will be responsible for driving improvements in how we govern, manage, transform, and deliver data. You will collaborate with cross-functional teams to design solutions to prevent recurrence and develop predictive models to identify and manage anomalies proactively.