A Senior Data Quality Analyst at Hayden AI is responsible for measuring data system performance, developing data quality standards, and collaborating with different engineering teams. You'll be involved in building reports and dashboards while investigating data patterns to identify risks. Your role also includes ensuring quality through safeguards and monitoring systems, making sure our products maintain high standards across diverse applications.

To be a Senior Data Quality Analyst at Hayden AI, candidates should have a B.S. in an engineering discipline or equivalent and at least 6 years of experience in data analysis or quality domains. We look for individuals who have worked on data products for 4 years or more and possess strong analytical skills paired with technical knowledge of data systems. Proficiency in SQL and familiarity with data quality frameworks are also essential.

Hayden AI ensures data quality and performance through a dedicated Quality Engineering team that designs and executes critical quality efforts. Senior Data Quality Analysts work closely with machine learning, cloud, and operations teams to set quality standards, measure performance, and report on key product indicators by using both manual and automated methods.

Senior Data Quality Analysts at Hayden AI work with a range of technologies, including SQL for data querying, Excel for analysis, and visualization tools like Tableau and Power BI for reporting. Familiarity with databases, data warehouses, and ETL processes is also critical for success in this role.

The Senior Data Quality Analyst role at Hayden AI is pivotal because it directly impacts the reliability and accuracy of our data-driven solutions. By ensuring high data quality standards, this position contributes to the overall mission of transforming public transit systems and enhancing safety, helping our clients make informed decisions based on trusted insights.

When answering this question, highlight specific frameworks you have worked with, detailing how you implemented them to drive data accuracy and reliability. Mention any improvements seen in data processing or decision-making as a result.

In your response, explain your systematic approach to identifying and analyzing anomalies. Discuss the tools and methods you use, such as SQL queries or analytical software, and describe a past experience where you successfully addressed a significant anomaly.

Effective communication is key in a Senior Data Quality Analyst role. Highlight your ability to simplify complex technical concepts into relatable terms for non-technical stakeholders. Provide examples of how you've successfully conveyed critical data quality issues or needs in past roles.

When addressing this question, focus on a specific instance where you encountered a significant data quality challenge. Describe the steps you took to understand the issue, the analyses conducted, and how you collaborated with team members to implement a resolution.

Explain your process for establishing data quality standards, including collaboration with cross-functional teams. Discuss how you take into consideration business needs, user requirements, and technical capabilities to create measurable and achievable quality benchmarks.

During your response, emphasize the importance of automation in improving efficiency and accuracy in data quality processes. Share any tools you've used, such as automated testing scripts or data validation tools, and how they have streamlined operations or reduced errors.

Provide details on specific data visualization tools you have used, such as Tableau or Power BI. Discuss projects where you created visual reports that provided actionable insights and how this helped in strategic decision-making.

In your response, define essential performance metrics like accuracy, completeness, consistency, timeliness, and uniqueness. Elaborate on how you apply these metrics in assessing data quality continuously and how they inform necessary adjustments in data processes.

Describe your approach to staying informed about the latest trends in data quality and analytics, such as attending workshops, webinars, or influencing blogs. Mention any industry publications or conferences you follow, and how you apply that knowledge to your work.

Focus on your passion for innovation in public transit systems and your alignment with Hayden AI’s mission. Talk about the company culture, its commitment to using advanced technology for societal good, and your desire to contribute to such impactful projects as a Senior Data Quality Analyst.