Analyze of the existing processes in CRM and connected systems in the aspect of monitoring the data quality
Identify strategies for measuring and improving data quality
Evaluate the quality of data received from data sources to ensure that they meet the needs of the organization
Create or update data models to ensure that data are stored in an organized manner
Develop and implement data quality metrics and KPI’s
Conduct regular checks and assessments of data quality using various metrics and analysis methods
Identify data issues such as duplication, incompleteness, inaccuracy, and inconsistency
Regular monitor and control of data quality using automated tools and systems
Create reports on data quality issues and recommending solutions to problem
Review data to identify patterns or trends that may indicate errors or inconsistencie
Identify causes of errors or discrepancies in data collection and recommending solutions
Identify and employing statistical methods relevant to data quality testing
Find and explore third-party data sources to enrich customer base
Together with business solution architecture work on business requirements for third-party data sources integrations with external systems (e.g. CRM)
Create BI dashboards to highlight data quality indicators and trends
Execute and automate test cases and perform bug tracking
Work closely with data quality analysts, developers and other stakeholders to understand data requirements
Develop of solutions for processing and analyzing large volumes of data
Prepare reports and presentations to communicate the insights and findings from the data to stakeholders, which can influence policy and decision-making processes
Requirements:
3+ year work experience in data cleansing projects
Hands-on experience with Data Quality tools (e.g. Demand Tool, Cloudingo)
Strong analytical skills and advanced Excel, SQL/NoSQL abilities
Advanced data visualization Power BI tool abilities
Knowledge of data analysis methods and machine learning
Advanced CRM skills as plus
Knowledge of data quality and data governance principles and standards
Understanding of data process flow
Data programming / coding Python or R – for automation and analysis