Lead/Principal Data Engineer

As a Lead/Principal Data Engineer at Rovio, you'll be at the forefront of designing, building, and optimizing both high-volume batch and soft real-time data pipelines. You'll play a key role in developing and maintaining the data infrastructure, ensuring that the data lakes, warehouses, and management platforms support robust business intelligence and analytics. Additionally, you'll implement cutting-edge data processing frameworks, collaborate with various teams, and stay ahead of industry advancements, making your role essential for enabling successful live operations within Rovio's games.

To thrive as a Lead/Principal Data Engineer at Rovio, strong programming skills in Python or Java are crucial, alongside solid proficiency in SQL for analytical queries. Experience with AWS data-related services and ETL tools, particularly Spark, is also essential. The role demands a proactive mindset, good time management, and the willingness to mentor and share knowledge. Additionally, having knowledge of Scala, and familiarity with Kafka and streaming analytics is a plus that enhances your candidacy.

At Rovio, continuous learning is baked into the culture. In your role as a Lead/Principal Data Engineer, you’ll have the opportunity to share knowledge, coach teammates, and stay on the leading edge of technological advancements in data engineering. Amidst a dynamic work environment focused on innovation, your insights into integrating new tools and technologies will not only help you grow but also influence the evolution of the data practices within the company.

As a Lead/Principal Data Engineer at Rovio, you will work with an impressive tech stack that includes Spark for processing and Python as your primary coding language. The role will also involve working with microservices and streaming analytics using Java and Scala, alongside query engines like Athena and Presto. Familiarity with AWS services such as EMR and SageMaker will also be key. This diverse and powerful stack supports the efficient management of data for millions of players.

Rovio is welcoming to candidates outside of Finland and offers relocation support for the right individuals. If you’re excited about making a move to the beautiful Helsinki Metropolitan Area while contributing to innovative gaming solutions, the relocation assistance provided by Rovio makes the transition smoother, allowing you to focus on your role as a Lead/Principal Data Engineer without the stress of moving logistics.

When discussing your experience with data pipeline design, focus on specific projects where you engineered efficient, scalable data pipelines. Highlight your role in optimizing performance and reliability, and explain how you addressed challenges throughout the design and implementation phases. Providing metrics or outcomes of your enhancements can further showcase the value you brought to your previous teams.

In answering this question, describe your strategies for ensuring data integrity and quality when handling large volumes. Share techniques such as real-time processing, batch processing, and the importance of monitoring and troubleshooting pipeline issues. Discuss how you collaborate with cross-functional teams to understand requirements and maintain high standards in data management, reinforcing your role as a proactive engineer.

Discuss your systematic approach when integrating new technologies into existing systems. Explain how you evaluate new tools, conduct trials, and address any compatibility issues. Emphasize collaboration with stakeholders to ensure that the integration aligns with business needs, and mention your process for measuring the impact and performance of new technologies once implemented.

In your response, highlight specific best practices you've implemented in data governance, such as defining data ownership, setting access controls, and maintaining accurate documentation. Discuss the importance of compliance with regulatory standards and how you ensure data accuracy through validation processes. Share insights on fostering a data-driven culture and how you engage with team members to uphold these standards.

Share specific experiences where you’ve successfully tuned ETL processes for performance improvements. Discuss metrics you monitored, like extraction times or transformation speeds, and the tools or methods you utilized for optimizing the performance. Mention collaboration with data stakeholders to identify needs and how ensuring efficient ETL processes contributed to overall data operations.

In discussing a challenging project, provide context about the objectives, the complexities involved, and your role in mitigating those challenges. Describe the methodologies you employed, the obstacles you encountered, and how you collaborated with your team to find solutions. Lastly, highlight the positive outcome or learnings from that experience, showcasing your problem-solving abilities.

Detail your hands-on experience with AWS, illustrating how you’ve used various services like EMR, SageMaker, or others relevant to data engineering. Discuss specific projects where cloud services played a critical role in managing data pipelines, and how you optimized costs and performance using AWS resources. Mention any certifications or training that back up your expertise.

Explain your commitment to ongoing learning in data engineering. Share the methods you use to stay informed, such as attending conferences, participating in webinars, following industry leaders on social media, and engaging in online forums or communities. Convey your enthusiasm for continuously acquiring new skills and your proactive approach to applying them in your roles.

Emphasize the importance of collaboration in your roles. Discuss how you engage with stakeholders from different teams, like Analytics and Data Science, to ensure alignment in data needs and objectives. Share specific examples of past projects where cross-department collaboration led to successful outcomes, showing your belief in teamwork as a driver for innovation and efficiency.

In your answer, touch upon systematic troubleshooting techniques you utilize, such as log analysis or monitoring metrics to pinpoint issues. Describe how you proactively identify potential failure points and what steps you take to rectify them. Illustrate your approach to maintaining data availability and consistency, reinforcing your role in ensuring operational excellence in data engineering.