Staff Data Engineer - job 26 of 50

As a Staff Data Engineer at Mentor, your primary responsibilities include acting as a technical bridge between the Data Engineering team and the Product organization. You'll engage in stakeholder meetings to clarify business needs, analyze data for database design, implement maintainable code according to best practices, conduct unit testing, and build automation processes. Additionally, you'll write queries for data extraction, monitor data processes, and ensure quality and performance throughout the data lifecycle.

To apply for the Staff Data Engineer position at Mentor, candidates should possess a strong technical background in data engineering, including experience with database design, coding, and data analysis. Familiarity with automation tools, data quality processes, and testing methodologies is essential. A bachelor's degree in Computer Science, Data Science, or a related field, along with relevant professional experience, will make you an ideal candidate for this role.

At Mentor, the Staff Data Engineer will report directly to the Data Engineering department and work closely with the Data Solutions Product Manager. You'll collaborate with various stakeholders, including Product teams and business partners, which creates a collaborative environment that fosters innovation and the successful integration of data engineering solutions into product initiatives.

The Staff Data Engineer role at Mentor is a hybrid position, allowing you to work both remotely and in the office. Employees are expected to be in the office 2-3 days a week, making this role a perfect combination for those who enjoy flexibility while still benefiting from in-person collaboration with the team.

A Staff Data Engineer at Mentor should be proficient in various tools and technologies relevant to data engineering, such as database management systems, coding languages for data manipulation (like SQL), and automation tools for enhancing data processes. Familiarity with cybersecurity practices, user acceptance testing, and self-healing processes in data pipelines is also advantageous.

When answering this question, be prepared to discuss specific projects where you designed or optimized databases. Talk about the methodologies you applied, such as normalization or star schema, and how you ensured that the database structure met business requirements effectively.

It's essential to demonstrate your communication skills here. Discuss a scenario where you translated complex technical issues into understandable terms for non-technical stakeholders, ensuring that everyone was aligned and well-informed about the project goals and requirements.

Highlight your coding best practices, such as following specific guidelines, adhering to naming conventions, and writing clean, modular code. Explain how these practices help minimize technical debt and improve collaboration with other team members.

You should discuss your methods for validating and cleaning data, implementing automated checks, and leveraging monitoring tools to detect anomalies. Explain how you prioritize data integrity throughout the data lifecycle.

Discuss a specific project that had hurdles, whether they were technical difficulties, tight deadlines, or miscommunications. Share how you analyzed the situation, collaborated with team members, and employed creative problem-solving skills to achieve the desired outcome.

Mention the various resources you utilize to stay informed, such as industry blogs, participating in workshops, or attending conferences. Engage the interviewer by discussing recent trends that excite you and how you see their application in the role.

Talk about the tools you’re familiar with, such as Apache Airflow, Apache NiFi, or other ETL tools. Provide examples of how you used these tools to create efficient automation solutions and what benefits they brought to the project.

Emphasize your openness to constructive criticism, recognizing it as an opportunity for growth. Share an example of a time when you received feedback and how you implemented changes to improve the overall outcome.

Discuss your role in UAT, detailing how you planned, conducted, and reported on tests carried out with end-users. Highlight the importance of gathering user feedback and ensuring that the final product meets the original requirements.

Provide insight into your SQL proficiency, discussing how you structure queries for better performance, manage joins and unions effectively, and ensure that the results returned are accurate and efficient. Highlight any tools or environments you utilize to test and optimize your queries.