Data Analyst, Partner Revenue Operations

As a Data Analyst in Partner Revenue Operations at Aura, your key responsibilities revolve around transforming source data for monthly billing, forecasting, and revenue recognition. You will work on ensuring enrollment numbers match financial records, assist in variance analysis, and contribute to ad-hoc projects vital for the revenue process. Collaborating with various stakeholders to uphold data accuracy and providing insights will be integral to your role.

To qualify for the Data Analyst role in Partner Revenue Operations at Aura, candidates should have 2-5 years of progressive experience in data analytics. A background in finance or accounting is a plus. Proficiency in SQL, experience with cloud data engineering platforms like Databricks, and advanced skills in Excel are also essential. Additionally, a strong understanding of financial statements and accounting principles is important.

Data Analysts in Partner Revenue Operations at Aura should be proficient in SQL for querying datasets, and have experience with Databricks for data manipulation in a cloud environment. Familiarity with tools such as Zuora, NetSuite, Salesforce, and Avalara would be beneficial, particularly in understanding billing processes and financial systems related to subscription-based models.

The role of a Data Analyst in Partner Revenue Operations is critical to Aura's growth by ensuring the accuracy of partner billing and forecasting processes. By aligning analytics with revenue generation and compliance standards, you directly impact financial performance and operational efficiency, helping Aura expand its partner channel effectively.

Aura provides a competitive benefit package for the Data Analyst position, including generous health and wellness benefits, retirement savings plans, parental leave, and more. Pay for this role ranges from $85,000 to $110,000, reflecting the experience and skill level required. Aura is committed to inclusion and ensuring every employee feels valued and supported.

When answering this question, highlight specific projects where you've used SQL to manipulate datasets. Discuss how you wrote queries to extract meaningful insights that influenced business decisions. Demonstrate your understanding of how SQL supports data integrity and efficiency in analytics, particularly in revenue operations.

Explain the steps you take to validate data, like implementing checks and reconciliations to confirm the accuracy of your datasets. You might mention using tools or software for data verification and your experience in identifying discrepancies and resolving them promptly.

Outline your process for tackling complex datasets, including data cleaning, analysis techniques, and visualization tools used. Share an example that illustrates your analytical thinking and how your findings benefited your team or organization.

Discuss your communication style and provide examples of how you've worked cross-functionally in the past. Highlight your ability to convey complex data insights in a digestible manner, which is crucial for alignment between analytics and operational strategies.

Mention specific blogs, forums, or training that you follow to keep your knowledge current. Discuss any recent tool or technology you have learned that could be relevant to the Data Analyst role, showcasing your commitment to continuous improvement.

Talk about the data visualization tools you have used, such as Tableau or Power BI, and how they helped you present your findings effectively. Share specific instances where your visualizations led to actionable insights or influenced stakeholder decisions.

This is a chance to showcase your financial knowledge. Discuss the principles of revenue recognition and how these impact billing processes. Be sure to relate them back to your experience and any roles you have had that required this level of understanding.

Illustrate your project management skills by sharing strategies you implement to prioritize tasks effectively. You can discuss how you plan your work, allocate resources, and remain flexible to meet deadlines without compromising data quality.

Describe your approach to variance analysis, perhaps detailing a systematic process you follow to identify disparities between expected and actual performance. Emphasize the importance of this analysis in informing future forecasts and aiding decision-making.

Discuss specific practices you implement to ensure accuracy in data work, such as double-checking entries, using validation rules, and peer review processes. Emphasizing your commitment to precision will resonate well with interviewers in a data role.