Lead Data Engineer

At Policy Expert, the Lead Data Engineer is responsible for driving the development and operation of a scalable data platform, enhancing the quality of data through effective ownership of data products, and leading a team of Senior Data Engineers.

To succeed as a Lead Data Engineer at Policy Expert, candidates should have proven experience in leading technical teams, designing scalable data platforms, and proficiency in data processing tools such as AWS Glue and Apache Spark. Strong skills in Python and SQL are also essential.

The Lead Data Engineer role at Policy Expert significantly impacts the insurance industry by developing innovative data solutions that optimize pricing strategies and enhance data-driven decision-making, thereby providing better value to our customers.

A successful Lead Data Engineer at Policy Expert should possess excellent leadership abilities, strong communication skills, and a collaborative mindset. The ability to translate technical concepts into business-friendly language is also crucial.

Lead Data Engineers at Policy Expert enjoy a range of benefits, including a generous pension scheme, private medical insurance, a learning budget, hybrid work options, and enhanced maternity and paternity leave.

Yes, at Policy Expert, Lead Data Engineers have access to a learning budget of £1,000 per year, encouraging continuous professional development and skill enhancement.

To succeed in the Lead Data Engineer interview process at Policy Expert, focus on showcasing your technical expertise, leadership experience, and ability to foster collaboration within teams, along with a genuine passion for innovation in the insurance industry.

When answering this question, highlight specific projects that involved the design and implementation of scalable data platforms, focusing on the tools you used, like AWS services or Apache Spark, and the challenges you overcame.

Discuss methods you employ, such as data validation, monitoring tools, and automated testing, to maintain high data quality. Provide examples of how these strategies have led to improved outcomes in previous roles.

Explain your leadership philosophy, such as promoting collaboration and feedback, setting technical standards, and encouraging continuous professional growth among team members, and provide examples of how you've successfully implemented this.

Provide details about a specific data product that you led the development of, its purpose, the technology used, and its benefits to the organization, quantifying results whenever possible.

Discuss various ways you keep up with industry trends, such as attending workshops, participating in online courses, or following relevant blogs or podcasts, and how this knowledge has influenced your work.

Share specific examples of cloud services you've utilized in your projects, explaining how they've helped you achieve scalability and efficiency in your data engineering work.

Discuss your approach to resolving conflicts, emphasizing open communication, active listening, and finding mutually beneficial solutions to foster a positive team environment.

Illustrate your understanding by discussing how event-driven architectures allow for real-time data processing and improving response times within projects, using specific examples when possible.

Explain specific techniques you use, such as parallel processing, efficient querying, or optimized data storage solutions, and any relevant experiences that demonstrate your success in this area.

Share your strategies for mentoring, such as conducting regular one-on-one check-ins, providing constructive feedback, and encouraging ownership of projects to develop their skills effectively.