Senior Data Analytics Engineer (Mobile BI and New Games)

As a Senior Data Analytics Engineer at Wargaming, you’ll be responsible for diving into player datasets to extract actionable insights, collaborating with product teams to track event performance, and creating dashboards to visualize metrics using tools like Tableau or Periscope. Your role is key in designing data models that support Game Analysts and sharing your passion for data through mentorship and presentations.

To qualify as a Senior Data Analytics Engineer at Wargaming, you should have at least 5 years of hands-on experience in analytics, ideally in the gaming industry. Proficiency in Python, strong SQL skills, and experience with cloud platforms such as AWS or GCP are also essential. Effective communication skills to present complex data clearly are crucial, alongside sharp analytical thinking and a keen attention to detail.

A Senior Data Analytics Engineer at Wargaming should have expertise in Python and SQL, with experience working on large datasets. Familiarity with cloud platforms like AWS or GCP is important. Additional knowledge of tools such as Snowflake, BigQuery, and dbt could be beneficial, especially if you have a background in mobile marketing analytics.

At Wargaming, the work culture is dynamic and fast-paced, perfect for a Senior Data Analytics Engineer who enjoys tackling multiple projects simultaneously. The team is focused on collaboration, with a strong emphasis on mentoring and knowledge sharing. We value flexibility and agility, allowing you to thrive in an innovative environment while contributing to exciting gaming projects.

As a Senior Data Analytics Engineer at Wargaming in Kyiv, you’ll enjoy a range of benefits, including 24 business days of leave, health care options, mental well-being programs, and personal development opportunities. Additional perks like English courses, company events, and a rewarding referral program further enhance your experience in our vibrant workplace.

When answering this question, highlight your past roles where you successfully analyzed large datasets. Discuss specific projects, the tools you used, and the insights you gathered that led to actionable changes or improvements. Demonstrating your ability to derive meaningful information from data is key.

Explain your methodology for gathering requirements from stakeholders, defining key metrics, and selecting visualizations that best represent the data. Discuss the tools you prefer and how you ensure the dashboard is user-friendly and provides actionable insights for product teams.

Articulate your prioritization process, mentioning techniques like using agile frameworks or project management tools to keep track of deadlines and deliverables. Highlight your communication with team members to ensure alignment and successful project completion.

Detail your usual practices including data validation methods, how you handle discrepancies, and the importance of establishing data quality checks. Mention any tools or techniques you use to automate these processes where possible.

Discuss your proficiency with SQL, including specific queries you've written to extract, manipulate, or analyze data. Perhaps showcase a challenging SQL problem you solved and how it impacted your team's work or the understanding of player behavior.

Describe a time when your data analysis provided insights that resulted in a substantial change in strategy or operations. Talk about the context, what you discovered, and the resulting improvements in game performance or player engagement.

Share your strategies for staying informed, such as attending industry conferences, participating in webinars, engaging with online courses, or reading relevant publications. Show your passion for ongoing learning and how it benefits your work.

Mention specific tools like Tableau or Periscope that you are experienced with, and discuss why you prefer them based on features, ease of use, or case studies from your experience. Talk about how these tools help to effectively communicate data insights.

Provide examples of how you've adapted to the agile methodology, focusing on teamwork and responsiveness to change. Discuss how this has influenced your role as a data analytics engineer and the outcomes of your projects.

Explain your approach to receiving feedback, emphasizing openness to constructive criticism and how you use it to refine your work. Discuss how collaboration enhances the team’s output and contributes to personal and professional growth.