Data Support Engineer

As a Data Support Engineer at Workday, your primary responsibilities will include ensuring timely resolution and maintenance of data pipelines and analytics solutions, especially those built on Snowflake and dbt. You'll also triage and investigate production failures and maintain data integrity across our platforms. In addition, you will engage with multiple stakeholders to provide analysis and solutions during incident management processes.

To qualify for the Data Support Engineer position at Workday, you should have at least 3 years of experience in building and supporting data and analytics pipelines. Familiarity with cloud-based tools like Snowflake and dbt is crucial, along with a strong grasp of advanced SQL and query optimization techniques. Proven experience in providing L2 support for data pipeline failures is also important, as well as effective communication skills to manage stakeholder relationships.

Workday is deeply committed to the growth and happiness of its employees, including Data Support Engineers. We foster an environment that encourages learning and provides access to modern analytics tools and technologies. You'll also have the opportunity to engage in on-the-job training, participate in cross-functional team projects, and attend workshops to enhance your skills and advance your career.

Data Support Engineers at Workday primarily work with a cloud-based technical stack including Snowflake and dbt, which are vital for managing data pipelines and analytics solutions. Additionally, you'll be using various modern analytics tools to improve data analysis and visualization, contributing to our mission of data-led innovation.

At Workday, we understand the importance of work-life balance. Our Flex Work policy allows Data Support Engineers the freedom to create a flexible schedule that meets both personal and professional needs. Team members are expected to spend at least half of their time each quarter in the office, while still having the flexibility to work remotely as necessary.

During your interview, highlight your experience using Snowflake specifically for data warehousing and pipeline management. Discuss the features you find most valuable, such as its ability to handle structured and semi-structured data, scalability, and performance optimizations. Share specific examples of how you've utilized Snowflake in past projects to demonstrate your hands-on skills.

To answer this question effectively, walk the interviewer through your troubleshooting process. Discuss how you would first triage the failure, perform root cause analysis, and then document your findings. Highlight the importance of maintaining communication with stakeholders throughout the process and using past incidents to suggest preventive measures.

Ensuring data integrity involves implementing validation checks, performing regular audits, and maintaining thorough documentation. In your response, explain the specific methods you use to verify data accuracy and how you collaborate with engineering teams to correct inconsistencies when found.

Choose a concrete example where you identified inefficiencies within a data pipeline and took actionable steps to improve it. Discuss the situation in detail: the problem, your analysis, the solutions you proposed, and the measurable results following implementation. This shows your proactive nature and capability in driving results.

Communication is key in a Data Support Engineer role. Describe your approach to simplifying complex technical concepts by using relatable analogies, clear visuals, and concise summaries. Illustrate this by sharing a story where your effective communication prevented misunderstandings or contributed to problem-solving.

When discussing a challenging project, focus on your challenges, the technologies involved, and your role in the project. Explain the actions you took to overcome obstacles, the skills you leveraged, and any lessons learned, emphasizing your adaptability and dedication to quality outcomes.

Outline your direct experience handling incidents, specifying your role during such situations. Discuss the importance of communication, structured processes, and collaborative efforts with team members to effectively resolve issues while minimizing downtime and ensuring stakeholders remain informed.

Talk about your commitment to ongoing learning, mentioning specific resources like online courses, webinars, and professional networks. Highlight any technology forums or industry events you participate in to stay at the forefront of data engineering trends, showcasing your initiative and passion for the field.

Share your strong familiarity with SQL and its critical role in your analytics work, outlining how you use SQL for data extraction, transformation, and analysis. Mention specific techniques you employ for query optimization to underline your technical proficiency and consideration for performance.

Express a positive attitude toward feedback and illustrate how constructive criticism helps you grow professionally. Provide examples of how you've implemented feedback from peers or stakeholders to enhance your work, thereby demonstrating your openness to learning and improving continuously.