Sr. Data Analyst

As a Sr. Data Analyst at Pilot, your core responsibilities will include performing in-depth analyses to bring new insights to light, influencing business decisions across departments. You’ll also create and maintain vital metrics and reporting using Looker, build efficient DBT pipelines for scalable analytics, and respond to ad-hoc data requests while aligning with long-term goals. Additionally, you’ll have the opportunity to train and mentor teams on effectively utilizing data in their decision-making processes.

To be considered for the Sr. Data Analyst role at Pilot, candidates should have over 5 years of relevant experience as a data analyst. A strong command of SQL is essential, along with experience in scripting languages such as Python and utilizing BI tools like Looker. Excellent communication skills will help you convey insights effectively, and familiarity with sales, marketing, or finance teams is highly favorable.

At Pilot, the work culture is collaborative and supportive, fostering a sense of community amongst data professionals. As a Sr. Data Analyst, you will have the freedom to explore creative solutions to complex problems while working closely with various stakeholders. We value the happiness and growth of our employees, offering flexible vacation policies and ongoing development opportunities that empower you to succeed.

Pilot is committed to investing in the professional development of our employees, including those in the Sr. Data Analyst role. We provide resources and opportunities for ongoing training, mentorship, and growth within an innovative environment. Our founders have successfully navigated multiple startups, providing a wealth of knowledge that can enhance your career journey.

A Sr. Data Analyst at Pilot should be proficient in SQL, Python, and BI tools such as Looker. Familiarity with data engineering tools like DBT, Snowflake, and Fivetran is a plus. Experience with Salesforce and Zuora data will also be beneficial. Being adept with these tools will enable you to automate data processes and deliver detailed insights.

When answering this question, emphasize your practical experience with SQL, including specific scenarios where you analyzed datasets, created complex queries, or optimized existing queries. Highlight the insights gained and how these influenced business decisions.

Discuss your methodology for creating clear and informative visualizations that cater to the audience's needs. Mention the tools you use, like Looker, and how you ensure the data presented is actionable and aligned with stakeholder goals.

Provide a detailed example, focusing on the problem you were tasked with, the analysis you conducted, and the eventual outcome. Highlight how your insights were pivotal in shaping the decision-making process.

Explain your approach to data quality, including validation techniques, data cleaning processes, and regular reconciliation practices to maintain high standards of accuracy and reliability.

Discuss your organizational strategies for managing requests, such as assessing urgency based on impact, collaborating with stakeholders to set priorities, and effectively communicating timelines and progress.

Share your mentoring philosophy and experiences. Highlight specific instances where you guided team members through their analyses, helping them develop skills while fostering a collaborative learning environment.

Mention specific resources you utilize to keep abreast of industry developments, such as webinars, online courses, professional blogs, or networking with others in the data analytics field.

Discuss your hands-on experiences with building and maintaining data pipelines, emphasizing any performance enhancements you achieved and how these pipelines have contributed to effective data analysis.

Be prepared to discuss specific challenges you encountered, what steps you took to navigate these issues, and the skills you developed through the experience.

Describe your technique for simplifying complex concepts and focusing on key insights to drive the message home. Providing context and relatable examples can help bridge the gap between technical and non-technical audiences.