Principal Data Engineer, Platform

As a Principal Data Engineer at Lila Sciences, you'll lead the design and implementation of our data storage strategy, focusing on creating a scalable data lake or warehouse for large scientific datasets. Your key responsibilities will include optimizing query performance, implementing data governance processes, and developing reusable code and APIs. Additionally, you’ll manage CI/CD pipelines and support infrastructure as code, all while collaborating closely with engineers and scientists to facilitate impactful scientific research.

To succeed as a Principal Data Engineer at Lila Sciences, you should have at least 5 years of experience managing data systems in production settings, along with strong Python coding skills. Experience with large-scale data solutions, excellent problem-solving abilities, and a team-oriented approach are crucial. Familiarity with workflow orchestration tools, data science libraries, and AWS services will also give you an edge in this role.

At Lila Sciences, collaboration is at the heart of what we do. As a Principal Data Engineer, you'll work within a highly cross-functional team, engaging with scientists, engineers, and other stakeholders to ensure that the data solutions you implement align with broader organizational needs. Your inclusive mindset and ability to communicate effectively across diverse teams will enable you to contribute to our mission in reshaping scientific discovery.

As a Principal Data Engineer at Lila Sciences, you'll leverage modern developer tools and technologies, including Python, data orchestration software like Airflow or Prefect, and cloud services such as AWS. Your technical expertise will also be utilized in managing CI/CD pipelines, database services, and observability tooling to monitor and optimize data performance.

The work environment for a Principal Data Engineer at Lila Sciences is dynamic, innovative, and supportive. We thrive in unstructured and creative spaces where ideas can flow freely, and all voices are valued. You’ll be surrounded by a passionate team of skilled professionals who are committed to advancing science through technology, ensuring that you have the resources and support needed to excel in your role.

When answering this question, highlight specific examples of large-scale data systems you've managed, detailing the technologies and architectures you used. Focus on the challenges you faced and how you overcame them while ensuring data integrity and performance.

Discuss methodologies you've utilized to enhance query performance, such as indexing, caching strategies, or query rewriting. Provide examples of performance metrics you've improved and demonstrate your analytical skills in assessing database performance.

Share your experience with data governance frameworks, including cataloging, lineage tracking, and quality assurance processes. Highlight specific practices that ensured compliance and data quality, emphasizing how these practices improved trust in data across the organization.

Emphasize your methods for fostering collaboration, such as regular stand-up meetings, using collaboration tools like git and CI/CD pipelines, and encouraging open feedback. Provide examples of how effective communication led to successful project outcomes.

Explain your philosophy on coding standards, documentation practices, and modular architecture. Discuss specific libraries you developed and how reusability has positively impacted team efficiency in project delivery.

Detail the AWS services you have worked with, how you’ve utilized them in data storage and processing, and your understanding of best practices for security and efficiency in the cloud. Providing specific projects can illustrate your hands-on experience.

Talk about the tools you’ve used for infrastructure as code (e.g., Terraform, CloudFormation) and your processes for automating deployment and minimizing manual configuration. Highlight your understanding of best practices in maintaining environments.

Describe the observability tools you've implemented (like Prometheus or Grafana) and the metrics you monitored. Highlight how these tools helped you improve system performance and troubleshoot issues effectively.

Discuss your commitment to clear and concise documentation. Explain the balance between technical documentation for engineers and user-friendly guides for end users, stressing how documentation can improve user experience and team productivity.

Convey your passion for data engineering and your enthusiasm for Lila Sciences' mission to transform science with AI. Highlight how your skills align with their innovative approach to data and your excitement to contribute to meaningful healthcare solutions.