Senior Data Analyst

As a Senior Data Analyst at Mae, your primary responsibilities will include defining and developing metrics to measure business performance, providing insights into product usage, sales, and customer behaviors, and translating complex data into clear narratives for diverse audiences. You will collaborate closely with the engineering team to build effective telemetry that informs decision-making.

To qualify for the Senior Data Analyst role at Mae, you should have at least 5 years of experience in data analysis, ideally within a product-focused environment. Proficiency in SQL and familiarity with analytical tools like Python or R is essential. Experience with healthcare datasets, user empathy, and a self-starter mentality will set you apart.

Success as a Senior Data Analyst at Mae is measured by your ability to reduce the time spent on manual data collection, develop insightful dashboards and analyses, and help your team meet engagement and revenue objectives. Your contributions will directly impact decision-making and drive the effectiveness of our product strategies.

In the Senior Data Analyst position at Mae, you'll work with a range of technologies including SQL for data management, advanced analytics tools like Python or R for statistical analysis, and BI tools such as Metabase and Excel. Familiarity with technologies like Typescript and Postgres will also be beneficial.

The work environment for the Senior Data Analyst role at Mae is fully remote, allowing for flexibility and work-life balance. You'll be part of a small, collaborative engineering team that values your insights and contributions while you work on projects that directly support the health and wellbeing of the community we serve.

When prioritizing tasks as a Senior Data Analyst, I assess the urgency and importance of each project or analysis request. Utilizing a framework like Eisenhower's Matrix can help in identifying which tasks align with business goals and require immediate attention, ensuring the most impactful work is prioritized.

In my experience with SQL, I have utilized it for data extraction, transformation, and analysis, creating complex queries to derive insights from large datasets. At Mae, I would leverage SQL to build and maintain the data structures that support metrics development and analytic reporting.

My approach to ad-hoc analysis involves first understanding the specific business question or problem that needs to be addressed. Then, I gather relevant data, apply appropriate analytical methods, and ensure I communicate findings in a concise yet insightful manner to stakeholders.

To translate complex data into a compelling narrative, I focus on understanding the audience and their needs. I start by identifying key insights and trends that resonate with them, then structure the information logically while using visual aids and storytelling techniques to enhance comprehension and engagement.

When faced with challenges related to data accuracy, I make it a priority to conduct thorough validation processes. This may involve cross-referencing data sources, performing integrity checks, and collaborating with other teams to ensure that the data used for analysis is reliable and trustworthy.

I have extensive experience conducting A/B testing where I design experiments to evaluate user behavior against variations in product features. I ensure that tests are statistically rigorous and analyze the results to derive actionable recommendations that influence product decisions.

To establish KPIs for a new product at Mae, I would begin by aligning with stakeholders to identify business objectives. Then I would analyze relevant user data and market trends, and finally, I would select measurable indicators that reflect the product's performance and user engagement.

In a prior role, I analyzed user engagement data which revealed drop-off points in the customer journey. By presenting these insights, we were able to redesign the user experience that led to a 25% increase in user retention, demonstrating the power of data in shaping product strategy.

I prefer using tools such as Tableau and Metabase for data visualization due to their user-friendly interfaces and robust functionalities. These tools allow me to create interactive, clear, and visually appealing dashboards that effectively communicate insights to both technical and non-technical stakeholders.

To stay updated with trends and technologies in data analytics, I regularly read industry blogs, participate in webinars, and engage with online communities. Joining data-focused meetups and attending conferences also help me gain insights into best practices and emerging tools in the field.