At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
The Data Quality Engineer will design and execute critical quality efforts for our connected platform and data service. In this role you will coordinate with machine learning, cloud, device, and operations teams to set, measure, and report on key product performance indicators.
The Quality Engineering team ensures that our product performs accurately, reliability, and consistently across a range of conditions and business rules. We work with a breadth of technologies including machine learning, embedded software, data pipelines, cloud infrastructure, web portal, and mobile hardware.
Measure data system performance end-to-end, using both manual and automated methods
Work with engineering teams to develop and set data quality standards
Build reports, dashboards, and queries for key performance indicators
Build a deep understanding of customer use cases to identify risk, and gather the right data to influence change
Investigate patterns and anomalies
Help develop data safeguards and monitoring systems to insure quality
B.S. in an engineering discipline or equivalent
6+ years experience in data analysis or data quality domains
4+ years of experience working on data products
Strong analytical and problem-solving skills with a keen eye for detail.
Demonstrated technical knowledge of product to be able to work with engineers.
Experience measuring performance of complex systems in real world applications
Track record driving data quality with multidisciplinary technical teams
Proficiency in SQL, Excel, and data visualization tools (e.g., Tableau, Power BI).
Experience working with databases, data warehouses, and ETL processes.
Familiarity with data quality frameworks, profiling techniques, and validation tools.
Ability to communicate technical concepts clearly to non-technical stakeholders.
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