Senior Data Engineer

As a Senior Data Engineer at Lighthouse, your responsibilities include designing and developing scalable data pipelines, optimizing performance, implementing data governance frameworks, and ensuring data quality and reliability. You'll collaborate with cross-functional teams to support innovative features and mentor junior engineers.

The Senior Data Engineer role at Lighthouse requires proficiency in Python, Java, or Scala, with a strong preference for Python. Familiarity with GCP, Kubernetes, CI/CD tools, and distributed data processing technologies like Apache Beam or Spark is essential for success in this position.

Lighthouse prioritizes career development for Senior Data Engineers by offering workshops, training programs, and a collaborative environment. You'll have access to resources that help you achieve your full potential and develop your skills in data engineering.

At Lighthouse, the team culture is collaborative, friendly, and creative. Senior Data Engineers will find themselves in a supportive community where teamwork and innovation thrive, creating an enjoyable work environment that pushes for growth.

Senior Data Engineers at Lighthouse will work with a range of technologies including Python, BigQuery, Airflow, Terraform, GCP, dbt, Kubernetes, and Kafka. This diverse tech stack allows for innovative data solutions to support our stakeholders.

When answering this question, share a specific project where you successfully designed and implemented a scalable data pipeline. Highlight the technologies used, the challenges faced, and how you ensured performance and reliability, demonstrating your problem-solving and technical skills.

Discuss your approach to data governance, validation techniques, and monitoring tools you employ to maintain high data quality. Providing examples from past experiences will showcase your commitment to delivering reliable solutions.

Highlight your hands-on experience with GCP by discussing specific services such as BigQuery, Cloud Storage, or any relevant tools. Mention projects where you utilized GCP to achieve business objectives or improve data processing efficiency.

Share instances where you mentored junior engineers, focusing on the techniques and resources you used to support their growth. Highlight the impact of your mentoring on their development and how it contributed to team success.

Reflect on specific challenges, such as scaling data pipelines or troubleshooting performance issues, and describe the strategies you employed to address those challenges efficiently and effectively.

Discuss the resources you follow, such as blogs, online courses, and professional organizations. Also, mention any conferences or community events you participate in to network and learn from other professionals in the field.

Elaborate on your experience with cloud-based data solutions by discussing platforms you've used and the types of projects you've tackled in the cloud. Focus on the benefits and challenges you've encountered to demonstrate your depth of knowledge.

Discuss your approach to data security, compliance with regulations, and implementing best practices, such as data encryption and access controls. Providing examples will demonstrate your understanding of the importance of data protection.

Emphasize the importance of collaboration in data engineering projects. Share examples of how you’ve worked with cross-functional teams, such as data scientists or product managers, to achieve common goals and improve data-driven decision-making.

Contextualize a specific data-related issue you faced, the steps you took to identify the root cause, and how you resolved it. Highlight the skills used and any lessons learned from the experience that can improve future troubleshooting efforts.