Data Quality Engineer

The Role:
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 of this role is to ensure high-quality data is 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. Principle responsibilities are to make data assets consistently accurate for users.

Responsibilities:

• 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 helps 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.
• Write DB scripts to validate data in the data repositories against the data in the source systems
• Track, monitor, and document testing results
• 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 with minimal supervision

Other key activities:

• Collaborate with Product Managers, developers, and data engineers to elicit, document, and translate business requirements for data needs into technical requirements while
  contributing to unified system architecture and minimizing technical debt
• Promote and support the infrastructure of reporting, analytics, and data sourcing strategy; this includes developing and advancing standards and best practices and recommending infrastructure changes when appropriate
• Perform ongoing monitoring and refinement of data platform
• Design and implement solutions to improve the data platform
• Collaborate on and learn new technologies
• Maintain, expand, and improve ETL processes
• Maintain data standards, enforce standard development protocols, and analyze requirements to ensure technical and standard operating procedure impacts are considered
• Troubleshoot and performance tune data management code
• Limit non-standard solutions and escalate when used with documentation supporting exception requirements
• Perform other duties as assigned

Work team approach:

• Work on a highly collaborative agile team
• Coordinate work with Database Administrators and System Engineers, as needed
• Assist report writers and data visualization team members with data sourcing
• Participate in technical reviews and provide detailed feedback for process improvement
• Collaborate with the Decision Services team, other members of Information Services and cross-functional business stakeholders to translate business requirements into technical specifications
• Effective communication skills with an ability to explain technical concepts to developers, product managers, and business partner
• Excellent problem solving and critical thinking skills
• Ability and desire to work with a team of people solving complex problems that often require independent research with minimal supervision
• Self-starter able to make an impact with little guidance
• Understanding of agile processes
• Outstanding attention to detail and ability to meet deadlines
• Self-starter with the ability to multitask in a dynamic work environment

Qualifications:

• Bachelor’s degree or master’s degree in a quantitative field such as Computer Science and Information Systems, Database Management, Big Data, Data Engineering, Data Science, Applied Math, etc.
• 3+ years of professional data analytics working experience. Experience with automotive data is a plus.
• 3+ years of experience with database software (SQLi Google Bigquery, etc), in writing complex SQL queries and stored procedures, and ETL in Python.
• 2+ years of experience working with large data and a variety of data sources.
• Experience working in virtualized cloud environments including cloud-based laaS/SaaS/PaaS solutions.
• Understanding of ETL methodologies and Data Warehousing principles, approaches, technologies, and architectures including the concepts, designs, and usage of data warehouses and data marts.
• Knowledge of data warehousing, OLAP, multi-dimensional, star, and snowflake schemas
• Knowledge and experience with database design principles including referential integrity, normalization, and indexing to support application development.
• Strong understanding and experience in development activities for all aspects of the Software Development Life Cycle (SDLC).

We will be giving a sign-on bonus of $300 and another $500 after passing 90 days in the company