Data Quality Engineer, Operational Platform

A Data Quality Engineer at Focus Financial Partners plays an essential role in ensuring the reliability, accuracy, and performance of data systems. Responsibilities include collaborating with data engineering teams, developing and executing test plans for ETL processes, performing end-to-end testing of data platforms, and automating testing processes using tools like dbt and Great Expectations. You'll also be tasked with identifying data quality issues and working closely with stakeholders to resolve them in a timely manner.

To be considered for the Data Quality Engineer position at Focus Financial Partners, candidates should typically have a bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent work experience. It's also crucial to have a strong understanding of data quality methodologies, as well as hands-on experience with SQL and data querying. Familiarity with big data technologies, cloud platforms, and testing automation tools is highly beneficial.

As a Data Quality Engineer at Focus Financial Partners, you will utilize a variety of tools to ensure data integrity and quality. These include ETL tools, databases, and testing frameworks such as dbt and Great Expectations for data validation. Knowledge of CI/CD pipelines and tools like Jenkins or GitLab will also be essential for automating workflows and testing processes.

Data Quality Engineers at Focus Financial Partners enjoy a collaborative work environment that can either take place onsite in St. Louis, MO, or remotely. You will be part of a dynamic and supportive team, working closely with data engineers, analysts, and other professionals to tackle challenges and enhance data quality across the organization.

Ensuring data quality as a Data Quality Engineer at Focus Financial Partners involves several strategies, including developing and executing comprehensive test plans, performing detailed validation of data across multiple systems, and monitoring data integrity continuously. Engaging in automated testing processes and collaborating with team members to identify and resolve defects promptly plays a significant role in maintaining high data quality standards.

When responding to this question, share specific methodologies you've used to ensure data quality, such as data profiling or automated testing strategies. Relate them to past projects and how they impacted the quality and reliability of the data delivered.

Discuss the steps you take in creating test plans, such as identifying critical data sources, outlining the testing process, and using tools for automation. Mention any specific ETL tools you’ve worked with and how you ensure coverage for edge cases.

Be prepared to talk about a specific challenge you've encountered, such as handling large volumes of data or dealing with unexpected API changes. Highlight your problem-solving approach, including how collaboration with others helped resolve the issue.

Detail any tools or frameworks you've used for testing automation, such as dbt or Great Expectations. Provide examples of how automation has improved efficiency and reduced manual errors in your previous roles.

Your answer should include strategies you employ for validation, such as cross-checking data outputs, establishing data governance practices, and utilizing monitoring tools to detect discrepancies.

Identify specific technologies like Hadoop, Spark, or Kafka and explain how you've utilized these technologies in your work as a Data Quality Engineer. Include any relevant projects that highlight your expertise.

When answering this question, mention visualization tools like Tableau or Power BI. Explain how you've used these tools to help users understand data issues or inaccuracies and how they assisted in validating data outputs.

Talk about any instances where you validated APIs or RESTful services in your previous roles, highlighting the importance of ensuring proper data integration and flow within systems.

Share a specific example of a data quality issue that required collaboration, detailing how you facilitated communication between technical and non-technical stakeholders, and what the outcome was.

Discuss the methods you use to stay informed about industry developments, such as attending webinars, following relevant publications, or participating in professional networks. Emphasize your willingness to adapt and learn new tools that can enhance data quality.