Data Quality Engineer

Tasks & Responsibilities:

• Responsible for automated data quality, risks and mitigations, and helping the team take ownership of data quality 
• Risk management and mitigation associated with data products (i.e. operational risk or privacy) 
• Planning and implementation of automated data quality tests, UAT (User Acceptance Test) and automated systems integrations tests 
• Creation and continuous improvement of automated data quality processes 
• Ensuring data quality through validation 
• Implementation of Built-In, automated Data Quality 
• Establishment of validation plans, designing and developing test frameworks for testing ETL jobs, BI reports and dashboards, and other data pipelines 
• Writing SQL scripts to validate data in BI assets against the data sources 
• Tracking, monitoring and documenting test results 

 Must Haves: 

• Educational background:  

1. MSc/BSc in a relevant field such as Computer Science, Data Science, Information Systems, or a related discipline. 

• Professional background:  

1. Previous experience in a data quality role or a related position. Experience with data quality processes, data validation, and data governance is highly valuable. 

• Hard skills:  

1. Proficiency in automated data quality. Strong analytical and problem-solving skills are essential for identifying and resolving data quality issues. 

• IT skills:  

1. Familiarity with IT systems and technologies used in data management, data querying, data integration tools, ETL processes, data warehouses, and databases.
2. Understanding the technical aspects of data pipelines and data management is crucial. 

• Language skills:  

1. Strong communication skills in English in both written and verbal forms are necessary for effective collaboration with cross-functional teams and stakeholders.
2. The ability to convey complex data quality concepts in a clear and concise manner is important. German knowledge is a plus. 

• Soft skills:  

1. Self-organization, autonomy, and an engaged mindset are vital for success in the role. Attention to detail, critical thinking, and problem-solving abilities are crucial for identifying and resolving data quality issues.
2. Collaboration, adaptability, and the ability to work in a team are valuable for effectively implementing data quality improvements. 

Nice to Haves: 

• Familiarity with data strategy and analytics: Understanding of data strategy and analytics concepts, including dashboards, reports, forecasts, and advanced data analytics solutions such as artificial intelligence and machine learning. 
• Data governance knowledge: Familiarity with data governance frameworks, policies, and procedures. Understanding of data standards and compliance requirements. 
• Understanding of data privacy and security: Knowledge of data privacy regulations and best practices for data security. Awareness of data protection requirements and ability to implement appropriate measures.