Senior Analytics engineer - job 1 of 2

The Senior Analytics Engineer at lempire will play a crucial role in driving data culture throughout the organization. Responsibilities include developing data models using DBT, managing the Business Intelligence solutions, and creating insightful dashboards. You'll work closely with teams across Sales, Marketing, and Product to answer data requests and improve overall data proficiency, ensuring that decisions are driven by data insights.

To qualify for the Senior Analytics Engineer position at lempire, candidates should have at least 5 years of experience in Data or Business Analysis within fast-growing environments. Proficiency in SQL, Python, and modern data tools such as BigQuery, Fivetran, and DBT is critical. Strong business acumen and the ability to challenge and support teams regarding their data needs are also essential.

The Senior Analytics Engineer has a significant impact on lempire’s growth by enabling data-driven decision-making across the organization. By developing robust data models and analytical dashboards, you will help teams clearly understand customer behavior, optimize operations, and refine marketing strategies, ultimately guiding our strategic initiatives and enhancing overall business performance.

At lempire, the work culture is focused on growth, collaboration, and innovation. As a Senior Analytics Engineer, you’ll be part of a supportive team that values your input and empowers you to define data initiatives. The company operates in a self-service environment, encouraging you to actively engage with other departments and contribute to the overall data strategy, fostering a culture where data is central to every decision.

At lempire, there’s robust potential for career advancement for Senior Analytics Engineers who demonstrate leadership and expertise. You may have the opportunity to build and lead your own data analyst team, as well as take on increased responsibilities within the organization. Consistent demonstrations of impact and strategic contributions can pave the way for further career growth in the company.

To develop a data model for marketing KPIs at lempire, I would start by collaborating with the marketing team to understand their key objectives. Next, I would identify the essential data sources and metrics needed to evaluate performance effectively. After that, I would construct the data model using tools like DBT, ensuring it captures all relevant dimensions and measures while also focusing on maintainability and scalability.

In my previous role, I recognized that the sales team lacked easy access to key metrics. I organized a series of workshops to understand their needs, created user-friendly dashboards in our Business Intelligence tool, and provided training on how to interpret the data effectively. This significantly improved their data visibility and empowered them to make more informed decisions based on insights.

To enhance lempire's data culture, I would focus on training and empowering teams to use data in their daily decision-making. This could involve holding workshops to familiarize all departments with our data tools and how to access necessary information. Additionally, I would advocate for regular sharing of insights across teams to foster collaboration and reinforce the idea that data drives our success.

When prioritizing data requests, I evaluate them based on urgency and potential impact on the business. I would consult with team leaders to understand the context—such as if it pertains to revenue generation or customer experience—then categorize the requests accordingly and set a timeline for fulfilling each request that aligns with business needs.

I have experience with various data analysis and modeling tools such as SQL, Python, DBT for data transformations, and Holistics for visualization. I also use cloud-based platforms like BigQuery for data warehousing and Fivetran for data integration. I've found these tools effective in managing and analyzing large datasets to derive insights.

In a previous role, I analyzed user engagement data to uncover trends indicating that certain features were underperforming. By presenting these findings to senior management, I was able to advocate for reallocating resources to prioritize improvement of those features, which led to a significant increase in user satisfaction and retention rates.

To ensure my code and data projects are maintainable, I adhere to best practices such as well-documented code, consistent naming conventions, and version control with Git. Additionally, I conduct code reviews with peers and dedicate time to refactoring and updating the documentation, which makes it easier for others to understand and modify in the future.

To understand customer churn, crucial metrics include the churn rate itself, customer lifetime value (CLV), and reasons for churn captured through surveys. Monitoring product usage, demographic shifts, and engagement scores can also provide valuable insights into potential churn risks, helping the team proactively address concerns.

If my data analysis contradicts widely held beliefs, I would approach the situation with an open mind and present my findings clearly. I would gather additional context behind the beliefs, engage in productive discussions with stakeholders, and invite them to explore the data together. Transparency and collaboration are key in driving data-informed changes while keeping an open dialogue.

I stay updated with the latest trends and technologies in data analytics by following reputable industry blogs, participating in online forums, and attending webinars and conferences. Additionally, I regularly explore new tools and methods, experimenting with them in personal projects, to continuously enhance my knowledge and skills in the evolving data landscape.