Senior Data Analyst

As a Senior Data Analyst at Zip, your responsibilities include developing data analytics solutions across departments, with a focus on Credit Risk and Finance. You'll partner with business leaders to enhance how Zip attracts and retains customers, create and maintain dashboards with tools like Tableau, and identify areas for optimization through data-driven recommendations. Your role will also require collaboration with data engineers to optimize data models that support the company’s growth in the fintech sector.

To be considered for the Senior Data Analyst position at Zip, candidates should have 5+ years of professional data experience, including strong proficiency in SQL and experience with a BI tool like Tableau. Previous experience building credit risk metrics in the fintech industry is essential. Ideal candidates will also possess excellent problem-solving skills and a strong desire to participate in a blameless culture.

The Senior Data Analyst role at Zip is designed to foster professional growth through hands-on experience and collaboration. You'll be encouraged to share ideas within the Data & Risk Team, work on complex analytical projects, and explore the latest industry trends. Zip offers a flexible working culture and professional development opportunities to elevate your expertise and career within the fintech space.

As a Senior Data Analyst at Zip, you will leverage various tools, specifically SQL for data querying, Tableau for visualization, and Atlan and Amplitude for analytics. You will also collaborate with data engineers to develop and optimize data models, providing valuable insights and supporting crucial business decisions.

Zip is an attractive employer for a Senior Data Analyst due to its commitment to innovation and a culture that values teamwork, learning, and individual accountability. The flexible working culture, generous benefits including 20 days of PTO and comprehensive health coverage, and the opportunity to work at a rapidly growing fintech company focused on customer success makes Zip a vibrant workplace for data professionals.

When building data dashboards in Tableau, I start by understanding the key metrics that stakeholders want to visualize. I ensure clarity in what the dashboard should represent and gather required data. After connecting the data sources, I use various Tableau features to create interactive visualizations that are user-friendly and insightful, allowing users to drive decisions based on the data presented.

In my previous role, I analyzed customer engagement data to identify patterns that showed a decline in retention rates. I presented these findings to management, suggesting targeted marketing strategies. As a result, the insight led to a 15% increase in customer retention over the next quarter. This experience highlights the importance of aligning data analysis with strategic business goals.

To ensure data integrity, I employ several techniques such as validation checks when importing data, using automated scripts to identify discrepancies, and regularly auditing data sources. I also engage in peer reviews, ensuring that findings are double-checked before they are reported or presented, guaranteeing that the insights I derive are reliable.

When faced with ambiguous data requests, I focus on communication. I ask clarifying questions to gather more context about the needs and objectives behind the request. Once I understand what the stakeholder is aiming to achieve, I can align my analysis accordingly, ensuring that I deliver actionable insights that meet the business requirements.

To identify trends in large datasets, I utilize both exploratory data analysis (EDA) techniques and visualization tools. By summarizing the data through statistical measures and leveraging tools like Tableau for visual representation, I can easily spot patterns and trends. Additionally, I apply segmentation methods to dissect the data further and analyze it from different angles.

My experience with SQL is extensive; I use it daily to extract, manipulate, and analyze data. I write complex queries to retrieve specific datasets, perform data transformations, and create aggregates that inform my analysis. I also use SQL to enhance data models by optimizing querying performance, ensuring that complex data sets can be efficiently analyzed and utilized.

When managing multiple data projects, I begin by assessing the urgency and impact of each task. I communicate with stakeholders to understand their timelines and needs, then prioritize based on project deadlines and overall business value. Utilizing project management tools helps me keep track of progress and adjust priorities as necessary to ensure all projects meet their deadlines.

To present data insights to non-technical stakeholders, I simplify the information by eliminating jargon and focusing on actionable insights. I incorporate visual aids like charts and graphs in presentations to clarify points visually. Moreover, I ensure to contextualize data insights within business implications, making it easier for stakeholders to grasp their relevance and potential impact.

In a previous role, I collaborated closely with a data engineer to optimize a data pipeline that processed sales data more efficiently. By clearly communicating the requirements for analytics, we redesigned parts of the pipeline to reduce processing time, which improved our reporting capabilities. This collaborative effort led to more timely insights for decision-making and enhanced team productivity.

I stay updated on the latest trends and tools in data analytics through continuous learning. I regularly attend webinars, participate in online courses, and follow industry blogs. Additionally, I participate in data analytics communities and forums where professionals share their insights and experiences. This proactive approach helps me keep my skills current and enhances my effectiveness in my role.