Assistant Manager

• Data Quality Strategy & Implementation:
• Develop, maintain, and continuously improve the data quality framework, ensuring it aligns with Lean principles and industry best practices.
• Design and track key data quality metrics, such as data accuracy, completeness, and timeliness, to measure data quality performance.
• Develop, implement, and maintain comprehensive data quality frameworks, policies, standards, and procedures in alignment with business objectives and regulatory requirements.
• Design and execute data quality assessments, profiling, and monitoring to identify data anomalies, inconsistencies, and root causes of data issues.
• Define and track key data quality metrics and KPIs, providing regular reporting on data health and improvement initiatives.
• Lean Methodologies & Process Improvement:
• Apply Lean principles (e.g., Value Stream Mapping, 5S, Kaizen, Gemba walks, Root Cause Analysis) to identify and eliminate waste in data creation, capture, processing, and consumption.
• Lead and facilitate workshops to streamline data-related processes, reduce errors, and improve data flow efficiency.
• Implement continuous improvement cycles for data quality processes, fostering a culture of ongoing learning and refinement.
• Develop and implement standardized work for data quality activities to ensure consistency and repeatability.
• Apply Lean principles (e.g., Value Stream Mapping, 5S, Kaizen, Gemba walks, Root Cause Analysis) to identify and eliminate waste in data creation, capture, processing, and consumption.
• Lead and facilitate workshops to streamline data-related processes, reduce errors, and improve data flow efficiency.
• Implement continuous improvement cycles for data quality processes, fostering a culture of ongoing learning and refinement.
• Develop and implement standardized work for data quality activities to ensure consistency and repeatability.
• Root Cause Analysis & Remediation:
• Perform in-depth root cause analysis for identified data quality issues, collaborating with all stakeholders
• Develop and implement corrective and preventive actions (CAPAs) to address data quality defects, ensuring long-term solutions.
• Work with data engineering and development teams to implement data quality rules and validations within data pipelines and applications/processes.
• Stakeholder Collaboration & Training:
• Provide training and mentorship to business users and data stewards on data quality best practices, tools, and processes.
• Champion data quality awareness and understanding across the teams.
• Tooling & Technology:
• Utilize and recommend data quality tools (e.g., data profiling, data cleansing, master data management, data governance platforms) to enhance capabilities.
• Work with IT to ensure data quality tools are effectively integrated and optimized.
  

Requirements:

• Experience: Minimum 3-5 years of experience in data quality management, data analysis roles with a focus on Lean practices and process improvement.
• Proven experience applying Lean methodologies (e.g., Value Stream Mapping, 5S, Kaizen, PDCA) to data-related processes and problem-solving.
• Strong understanding of data quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness).
• Lean Certification: Certified Lean Practitioner (CLP) or equivalent certification. Lean Six Sigma Green Belt
• Strong communication (written and verbal) and interpersonal skills, with the ability to articulate complex data concepts to non-technical stakeholders.
• Ability to work independently and collaboratively in a fast-paced, dynamic environment.
• Analytical: Strong analytical and problem-solving skills, with the ability to analyze complex data quality issues.
• Collaboration: Ability to work collaboratively in a team environment, with a focus on continuous improvement.