- Ataccama ONE Implementation:
- Leverage the features of Ataccama ONE to establish and maintain robust data quality processes.
- Implement data remediation processes and DQ workflows across various domains, ensuring data integrity and consistency.
- Develop and manage Data Quality scoreboards and dashboards to monitor and report on data health metrics.
- Utilize Ataccama's AI-driven capabilities to automate and enhance data quality management. Ataccama
- Data Remediation:
- Identify and rectify data quality issues at the source, implementing corrective actions to prevent recurrence.
- Collaborate with data owners and stewards to ensure adherence to data quality standards and policies.
- Data Observability:
- Monitor data pipelines and systems to detect anomalies, ensuring timely interventions to maintain data reliability.
- Implement tools and processes for continuous data monitoring, leveraging Ataccama's data observability features. Ataccama Docs
- Augmented Data Quality:
- Apply AI and machine learning techniques to enhance traditional data quality processes, enabling proactive data management.
- Stay abreast of industry trends and best practices in augmented data quality to drive continuous improvement. Gartner
Required Skills:
- Extensive experience in implementing and managing data quality solutions using Ataccama ONE.
- Robust understanding of data remediation processes and the ability to address data quality issues at their source.
- Proficiency in developing and utilizing data quality scoreboards and dashboards for monitoring purposes.
- Familiarity with data observability concepts and tools to ensure comprehensive data monitoring.
- Knowledge of augmented data quality methodologies, including the application of AI and machine learning in data quality management.
- Excellent analytical and problem-solving skills, with a keen attention to detail.
- Strong communication and collaboration abilities to work effectively with cross-functional teams.
Preferred Qualifications:
- Certification in Ataccama ONE or related data quality tools.
- Experience in data governance and master data management practices.
- Background in industries with stringent data quality requirements, such as finance, healthcare, or telecommunications.