Data engineer manager / BI manager

As a Data Engineer Manager / BI Manager at LoopMe, your main responsibilities include building and maintaining an internal data repository to support BI tools like ThoughtSpot and Tableau, leading a team of data engineers, and optimizing data pipeline architectures. You'll also work closely with the Product Team to identify the core data needs based on input from Finance and Business Development teams, ensuring that our data is accurately organized and readily accessible.

To be considered for the Data Engineer Manager position at LoopMe, candidates should have at least 5 years of experience in a data engineering role, with a minimum of 2 years in a managerial capacity. Proficiency in database technologies such as Clickhouse, Postgres, and ElasticSearch is essential, as well as hands-on experience with SQL or NoSQL databases and cloud-based technologies like GCS and AWS.

LoopMe is committed to the professional growth of our Data Engineer Managers by offering a training budget to enhance skills and knowledge. Additionally, we promote a culture of collaboration and innovation, where team members can share ideas and strategies for improvement within a flexible and supportive work environment.

At LoopMe, you can expect an international work environment that values diversity and creativity. We offer a hybrid working model, allowing you flexibility in balancing work and life responsibilities, along with an attractive compensation package that includes performance bonuses and a unique workation opportunity to work from any location worldwide for a month.

In your role as Data Engineer Manager at LoopMe, you will work with a variety of cutting-edge tools and technologies, including ThoughtSpot and Tableau for BI reporting, alongside data storage solutions like Clickhouse, Postgres, and Elastic Search. Your expertise in these tools will allow you to build optimal data systems and ensure the reliability and accuracy of data flowing into these environments.

When answering this question, discuss your familiarity with database technologies like Clickhouse, Postgres, and Elastic Search. Be specific about how you have effectively utilized these databases in past projects, detailing any challenges faced and how you overcame them. Highlight your technical expertise and any innovative solutions you developed.

A strong answer to this question would include a step-by-step overview of your process for designing a data pipeline. Discuss your considerations regarding data sources, transformation methods, and how you ensure the accuracy and reliability of the data. Mention any tools or frameworks you've used effectively in previous projects.

Share specific strategies you employ to ensure data quality, such as regular audits, implementing validation checks, and collaborating with stakeholders to define data requirements. Highlight any tools or best practices you rely on to monitor and maintain data integrity across various systems.

Show your commitment to continuous learning by mentioning the resources you use to keep abreast of industry trends, such as online courses, webinars, or professional groups. Talk about how you’ve implemented recent developments in your work to enhance data processes at your previous job.

When discussing a past project, outline your leadership role, the project's goals, the challenges faced, and the outcomes achieved. Emphasize your decision-making process, teamwork, and how you facilitated communication and collaboration within your team to meet project objectives.

In your response, describe your hands-on experience with cloud computing technologies, focusing on specific projects where you’ve utilized GCS or AWS. Explain how cloud technology improved the efficiency, scalability, or security of data handling and the overall impact on your engineering processes.

Highlight your management style and various mentoring strategies you employ to nurture talent among your team. Discuss how you foster a positive working environment, encourage skill development, and ensure team members feel valued and engaged in their work.

Describe your collaborative process with product managers, emphasizing the importance of understanding their requirements and aligning data initiatives with broader business goals. Share examples of how you've successfully partnered with product teams in the past to enhance data-driven decision-making.

In answering this question, provide a specific example of a time when you faced a tight deadline. Explain how you prioritize tasks, manage your time effectively, and communicate with your team to meet those deadlines without compromising data quality.

Discuss your favorite tools for data visualization, such as Tableau or ThoughtSpot, and explain why you prefer these tools based on their features, usability, or the specific needs of your team. Provide examples of how these tools have effectively communicated data stories or insights in past work experiences.