Data Analyst

As a Data Analyst at Archive, your key responsibilities will include architecting data models to drive performance metrics, developing reporting practices that enable informed decision-making for marketing and growth initiatives, and creating dashboards for customer analytics about program performance. You will also be tasked with handling ad hoc data requests while managing long-term project deliverables, ensuring that you contribute significantly to Archive's mission of improving shopping behavior.

To become a Data Analyst at Archive, candidates should have proficiency in SQL and Python, alongside experience in designing and building dashboards. A strong statistical background, attention to detail, and clear communication practices regarding data insights are crucial for success in this role. While not mandatory, having experience with data management systems, data warehouses, and modern data languages like Spark will be considered a bonus.

The Data Analyst role at Archive leverages a modern technology stack which includes SQL, Python, and Spark for data processing and analysis. Our database of choice is PostgreSQL, while we utilize platforms like Palantir Foundry, Hex, and Snowflake for our data projects. Additionally, our infrastructure is built on AWS, providing a robust environment for our data operations.

The expected annual base salary range for the Data Analyst position at Archive is between $110,000 and $190,000. This range reflects various factors including role level, experience, skills, and location. Furthermore, eligible roles may include equity options, and all employees can take advantage of a comprehensive benefits package that encompasses healthcare and 401(k) enrollment.

Archive prides itself on cultivating an inclusive and transparent culture, a principle that is especially important for our Data Analysts and all team members. We deeply value diverse backgrounds and encourage applicants who may not meet every criterion listed for the position to still apply. With regular company offsites and a remote-first work environment, we ensure that all employees feel connected and engaged, embodying our mission to drive positive change in the retail industry.

In previous roles, I have extensively used SQL to manipulate and query databases for analytics. I can discuss a specific project where I designed complex queries to extract meaningful insights from large datasets, ensuring that I prepared accurate reports that facilitated data-driven decision-making.

When building a dashboard, I first identify the key performance indicators to track. I then gather requirements from stakeholders and design the layout for clarity and usability, leveraging tools such as Tableau or Power BI for visualization. Prioritizing simplicity, I ensure the dashboard is actionable and provides insights that can drive business strategies.

To ensure attention to detail, I adopt a systematic approach where I meticulously check each step of my data processing pipeline. I also conduct peer reviews to validate findings and use data validation techniques to catch any discrepancies. By maintaining comprehensive documentation, I can trace back my steps if any issues arise.

I believe that clear communication is key when sharing data insights with non-technical team members. I strive to simplify technical jargon into relatable concepts while using visuals and storytelling techniques to illustrate the impact of the findings. This not only enhances understanding but also encourages collaborative decision-making.

In a previous role, I encountered a situation where an ad hoc request came in with a 24-hour deadline. I quickly prioritized the key data points required, streamlined my data extraction processes, and communicated effectively with stakeholders on the status. By efficiently managing my time, I delivered the analysis on schedule, which helped support urgent business decisions.

Working with large datasets can often lead to performance issues. To overcome these challenges, I have utilized techniques like data sampling and aggregating data to increase processing efficiency. Additionally, I employed optimized SQL queries and took advantage of scalable cloud solutions to ensure my analyses could be executed swiftly.

I have hands-on experience with several data visualization tools such as Tableau, Power BI, and Google Data Studio. My preferred choice is Tableau due to its robust functionality and flexibility in creating interactive dashboards, which allows end-users to explore data insights dynamically.

Ensuring accuracy and reliability starts with data sourcing; I verify the integrity of data before analysis. During analysis, I use statistical methods to detect anomalies and regularly validate my findings against secondary sources or benchmarks. I believe in conducting thorough checks throughout the entire process.

In one project, I conducted analysis that revealed a significant drop in customer retention rates. By presenting these insights to the marketing team, we could adjust our customer engagement strategies, leading to a 20% improvement in retention rates within three months. This outcome not only drove revenue but also increased overall customer satisfaction.

I'm drawn to the Data Analyst position at Archive because of your commitment to driving sustainable consumer behaviors through data. I resonate deeply with the mission of reducing waste in the retail industry and am excited about the opportunity to apply my analytical skills to support such a meaningful cause and engage with an innovative team.