Data Analytics Engineer

To be considered for the Data Analytics Engineer role at Tovala, candidates should have 1-3 years of relevant experience in analytics engineering, data engineering, or data analytics. Proficiency in SQL and experience with data warehouse environments, preferably Snowflake, are essential. Familiarity with conceptual data modeling and hands-on experience with tools like dbt would set candidates apart. Strong collaboration and communication abilities, along with experience in data quality assurance and version control, are also valuable for this position.

As a Data Analytics Engineer at Tovala, your primary responsibilities will include building and maintaining trusted data models for analysis, increasing data accessibility for stakeholders, ensuring data quality, and monitoring data systems for issues. You will document processes and collaborate with data scientists and analysts to enhance the usability of our data, allowing for faster insights and data-driven decision-making across the company.

Tovala is committed to building a unique culture that encourages personal growth and development within our Data Analytics Engineer team. We emphasize continuous improvement by staying informed about emerging data trends and technologies, promoting knowledge sharing through code reviews and documentation, and providing opportunities to learn and collaborate with various teams across the organization.

At Tovala, the values we cherish greatly influence the Data Analytics Engineer role. We believe in putting the team first, delivering high-impact work, fostering curiosity, sharing perspectives openly, and embracing challenges. We prioritize our customers in all decisions, which helps shape the interpersonal dynamics within our team and drives us to excel at what we do.

A Data Analytics Engineer at Tovala should have strong skills in SQL and familiarity with data warehouse platforms, ideally Snowflake. Experience with dbt for data modeling is crucial. Additionally, familiarity with data visualization tools like Sigma, Tableau, or Power BI is a plus. Understanding marketing data and segments such as CRM, attribution, and campaign performance metrics is also valuable in supporting our growth marketing efforts.

In responding to this question, emphasize specific instances where your SQL skills have significantly influenced data modeling. Discuss complex queries you’ve written, how you optimized performance, or any instances where you collaborated with teams using SQL to derive actionable insights. Highlight any particular projects where your SQL expertise directly contributed to decision-making.

When addressing this question, discuss your methodology for implementing data quality checks, monitoring systems, and validating data accuracy. Share examples from past experiences where you identified and rectified data inconsistencies, ensuring that your data models yield reliable outcomes, particularly during critical projects.

Highlight the importance of communication and teamwork in your response. Offer examples of collaborative projects where you actively engaged with data scientists and analysts, and convey how you translated business requirements into scalable data solutions. Focus on illustrating your adaptability and willingness to incorporate feedback from colleagues.

Discuss your experience with various data visualization tools like Tableau, Power BI, or Sigma. Provide specific examples of how you utilized these tools to present data cleanly and understandably, and how this contributed to decision-making within the teams you worked with.

Respond by detailing your hands-on experience with dbt, emphasizing how it facilitates modular workflows and enhances data transformations. Share insights on projects where dbt improved your data models' efficiency, consistency, or simplicity. This showcases your technical proficiency and understanding of best practices in analytics.

Discuss the importance of clear and comprehensive documentation in your workflow. Share techniques you implement to regularly update documentation, such as code comments, maintaining a knowledge base, or collaborative documentation efforts. Express your commitment to knowledge sharing as a way of fostering a continuous learning environment.

Provide a specific example of a complex data challenge, details surrounding the issue, your thought process in approaching the resolution, and what you learned from the experience. Focus on your problem-solving skills and ability to stay composed under pressure as you navigated the situation.

Share your strategies for keeping current with the latest trends in the analytics field, such as participating in webinars, following leading blogs or thought leaders, attending industry conferences, or being part of relevant online communities. This shows your proactive approach to continuous learning.

Highlight the various strategies you employ to make data more user-friendly, such as data visualization best practices, simplifying complex data sets, or creating intuitive dashboards. Provide examples of how these have enabled stakeholders to derive insights more efficiently.

Elaborate on your approach to enhancing data accessibility, which might include organizing and cataloging datasets, utilizing user-friendly self-serve data models, or working directly with teams to understand their needs. Discuss your experiences in previous roles and illustrate the impact of improved accessibility on business decision-making.