Senior Analytics Engineer II

As a Senior Analytics Engineer II at Compass, you'll be responsible for architecting and optimizing analytical solutions that transform raw data into actionable insights. You'll work closely with cross-functional business users to translate their requirements, collaborate with data engineering teams to build data models and pipelines, and improve our existing analytics infrastructure. Additionally, you will develop dashboards and reports that effectively communicate insights to both technical and non-technical stakeholders.

To qualify for the Senior Analytics Engineer II role at Compass, you'll need at least 6 years of relevant experience in engineering or analytics. Strong skills in SQL, DAX, and data modeling are crucial, along with proficiency in data transformation using SQL or Python. Experience with visualization tools like Tableau or Power BI, as well as working with cloud-based data lakehouses such as Databricks or Redshift, is also important.

The Data & Analytics team at Compass plays a vital role in fostering a data-driven culture. They build scalable analytical platforms that drive operational excellence and support critical business intelligence and data science use cases. As a member of this team, you'll collaborate with various departments to ensure that data solutions meet the needs of all stakeholders and empower a wide range of data-driven decisions.

In the role of Senior Analytics Engineer II at Compass, you'll enhance your skills in advanced data modeling, analytics architecture, and data visualization. You'll gain experience working closely with various business users, honing your ability to translate functional requirements into scalable solutions. Additionally, you will develop your mentorship skills by guiding junior team members and expand your expertise in using various analytics and data engineering tools.

Compass fosters a collaborative, energetic work culture that highly values diversity, equity, and inclusion. As a Senior Analytics Engineer II, you’ll be part of an environment that encourages innovation, shared learning, and professional growth. The company promotes a sense of belonging, allowing team members to be their authentic selves while making significant impacts through data.

When answering this question, focus on specific projects where data modeling played a central role. Discuss the tools and methodologies you used, the challenges you faced, and how you overcame them. Highlight your understanding of data integrity, efficiency, and how well-structured data models facilitate better analytics and reporting.

To tackle this question effectively, describe your approach to prioritizing based on project deadlines, impact on business decisions, and alignment with team goals. Emphasize the importance of communication with stakeholders to ensure alignment, and mention tools or strategies you use for task management.

Explain your methods for verifying data accuracy, such as data validation processes, consistency checks, and peer reviews. Emphasize your attention to detail and the techniques you use to mitigate errors, ensuring that stakeholders can rely on the insights derived from your reports.

Use the STAR method to outline the Situation, Task, Action, and Result. Focus on a complex problem, the innovative solution you implemented, and how it positively impacted your team's ability to make informed decisions or improve performance.

Demonstrate your commitment to ongoing learning by mentioning specific resources such as online courses, webinars, and professional groups you follow. Sharing anything specific you’ve learned recently and how it applies to your work can also strengthen your answer.

Highlight your ability to communicate effectively with team members from different functions. Illustrate how diverse perspectives were valuable in meeting project objectives, and showcase your collaboration techniques, such as regular check-ins or feedback sessions.

Be specific about the tools you have used and your reasons for preferring them. Discuss how they have facilitated your work in data transformation, analysis, or visualization. Highlight any unique features that make these tools particularly beneficial in your previous roles.

Provide insights into key performance indicators that can reflect the effectiveness of analytics solutions, such as adoption rates, accuracy levels, and user satisfaction. Discuss how these metrics can offer valuable feedback for continual improvement.

When addressing this question, stress the importance of collaboration and support. Discuss your mentorship style, focusing on providing constructive feedback, fostering a growth mindset, and facilitating opportunities for learning on real projects.

Share an example where stakeholder feedback directly influenced your analytics deliverables. Discuss steps you took to implement changes, the impact on project outcomes, and how it enhanced future interactions with stakeholders.