Data Quality Engineer

Data Quality Engineer

• 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 
• English language (Upper-Intermediate)

Main responsibilities:

• 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