Data Analytics Engineer

As a Data Analytics Engineer at Cerebras Systems, you will be responsible for collaborating with stakeholders to gather requirements, writing optimized SQL queries, and developing interactive dashboards using Grafana. Your role includes performance optimization for both dashboards and queries, ensuring they handle large datasets efficiently while delivering actionable insights across the organization.

To apply for the Data Analytics Engineer position at Cerebras Systems, candidates should possess a Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Additionally, a minimum of 3 years of experience in data analytics or business intelligence, strong SQL skills, and proficiency in Python are essential to succeed in this role.

A Data Analytics Engineer at Cerebras Systems should be well-versed in SQL for complex query writing and performance tuning. Familiarity with dashboard development tools like Grafana is essential, as is experience with Python for data manipulation. Knowledge of working with Postgres databases, time series data, and monitoring tools is also advantageous.

The Data Analytics Engineer plays a crucial role at Cerebras Systems by providing data-driven insights that facilitate informed decision-making across various teams. By delivering high-quality dashboards and optimizing data queries, this role enhances the overall efficiency of business processes, ultimately supporting the company’s mission to revolutionize AI applications.

Cerebras Systems fosters a unique work culture that emphasizes individual growth and a non-corporate atmosphere. Data Analytics Engineers can expect to be part of a supportive environment that values diversity and inclusion, encourages innovative thinking, and offers stability while being part of a groundbreaking AI initiative.

In preparing your answer, focus on the SQL query's purpose, the data sets it operated on, and the outcome it achieved. Highlight your thought process and any challenges you encountered during the development, such as performance issues and how you optimized for better results.

Discuss specific projects where you created dashboards using Grafana or similar tools. Explain the data sources you pulled from, the visual elements you implemented, and how your dashboards aided users in decision-making, ensuring to include feedback you received.

Detail your strategies for maintaining data integrity, such as regular monitoring, validation checks, and the processes you follow for requirement gathering from stakeholders to ensure their needs are met, leading to accurate outcomes.

Identify a specific challenge, ideally related to data complexity or performance issues. Highlight your problem-solving approach, the steps you took to troubleshoot, and the successful results that followed, emphasizing your analytical skills.

Explain your communication and organizational skills. Discuss how you prioritize requirements based on stakeholder interviews, feedback loops, and your methods for keeping all parties informed throughout the analytics lifecycle.

Share specific techniques you've utilized, such as indexing, avoiding subqueries, using joins effectively, and analyzing query performance metrics. Illustrate how these optimizations have enhanced the efficiency of your data-extraction processes.

Mention the resources you use such as industry blogs, webinars, online courses, and professional groups. Discuss how you apply new learnings to your current work and share examples of innovations you've participated in.

Provide a brief overview of a project where you leveraged Python for data extraction, cleaning, or visualization, focusing on libraries like Pandas or NumPy. Emphasize how your work improved the data flow and analysis accuracy.

Explain your understanding of user needs and the importance of user experience. Share your process in designing custom visualizations, including tools used and the reasoning behind selecting specific formats to convey data clearly.

Discuss any specific projects that involved time series analysis. Outline the methods you utilized to analyze trends or forecast patterns, and how those insights influenced business decisions or project outcomes.