Data Quality Engineer

• The Data Quality Engineer is responsible for designing, developing, documenting, and performing data quality checks across all data assets.
• That includes ETL jobs, reports, dashboards, and data pipelines. The primary goal for this role is to ensure high quality of data delivered to internal stakeholders and customers.
• Validation of data in data repositories against data from source systems and validation of metrics and data in reports/dashboards against data in the repositories is a key responsibility to ensure data assets are consistently accurate for users.
• The Sr. Data Quality engineer will engage in all data related activities such as data profiling, cleansing, deduplication, standardization, and conversion.

• Work in conjunction with Developers and Data Engineers to ensure high quality Data Deliverable
• Working with the development teams from different groups in the organization, help identify inconsistent data patterns, and how they are manifested from the source processes.
• Design, implement and use data quality assurance frameworks to support the process of identifying inconsistent data patterns.
• Analyze complex data systems to develop automated and reusable solutions for extracting requested information while assuring data validity and integrity.
• Perform tasks spanning the full lifecycle of data management activities including but not limited to defining completeness, accuracy, and consistency specifications by data element, writing scripts, and developing tools to monitor quality, developing impact analysis, building data lineage to downstream reporting and analytics, building defining and implementing controls for key data quality measures, and producing interactive data quality dashboards.
• Derives data management structures and metadata to support consistency of information retrieval, combination, analysis, pattern recognition and interpretation, throughout the organization.
• Perform ongoing monitoring and refinement of the Data Lakehouse.
• Maintain data standards, enforce standard development protocols, and analyze requirements to ensure technical and standard operating procedure impacts are considered.
• Limit non-standard solutions and escalate when used with documentation supporting exception requirements.
• Assist report writers and data visualization and team members with data sourcing.
• Collaborate with members of Information Technology, and cross-functional business stakeholders to translate business requirements into technical specification into measurable outcomes.

• Understanding of ETL methodologies and Data Quality principles, approaches, technologies, and architectures including the concepts, designs, and usage of data warehouses and data marts.

• Knowledge of Power BI development best practices
• Knowledge of data warehousing, OLAP, multi-dimensional, data vault methodology and snowflake schemas
• Strong understanding and experience in development activities for all aspects of Software Development Life Cycle (SDLC). Data Vault Methodologies (nice to have)
• Excellent problem solving and critical thinking skills
• Effective communication skills with an ability to explain technical concepts to developers, product managers, and business partner.
• Knowledge and experience with database design principle including referential integrity normalization, and indexing to support application development

• Bachelors degree or masters degree in a quantitative field such as Computer Science and Information Systems, Database Management, Big Data, Data Engineering, Data Science, Applied Math, etc.
• 5+ years of professional data analytics working experience. Experience with automotive data is a plus

• 3+ years of experience working with large data and variety of data sources.
• Experience working in virtualized cloud environment including cloud-based IaaS/SaaS/PaaS solutions
• Experience in Power BI, and SQL

Harvey Nash