Data Engineer

At Bask Health, the Data Engineer is responsible for designing, constructing, and maintaining a robust data infrastructure that supports our telehealth platform. This includes building scalable and efficient data pipelines, developing data models for analytics, and creating patient-facing data products to improve healthcare delivery. Your role will also involve collaborating across teams to ensure informed decision-making based on well-organized data.

The Data Engineer role at Bask Health requires a Bachelor’s degree in Computer Science, Data Engineering, or a related discipline. Candidates should possess 3-5+ years of relevant professional experience, particularly in the healthcare or tech industry. Demonstrated skills in designing scalable data pipelines and familiarity with tools such as Apache Airflow and cloud platforms like AWS or GCP are crucial for success in this position.

To be effective as a Data Engineer at Bask Health, candidates should be advanced in SQL and familiar with programming languages such as Javascript or Typescript. Skills in ETL/ELT processes and experience with data orchestration tools like dbt or Luigi are highly valued. Additionally, a strong understanding of data modeling, schema design, and experience handling streaming data frameworks will be beneficial.

A Data Engineer at Bask Health contributes significantly to healthcare innovation by developing and optimizing data solutions that enable informed decision-making. Through efficient data pipelines, they ensure accurate and timely data delivery, aiding product teams in creating innovative patient solutions. This role directly impacts the quality of healthcare insights and patient experiences, driving the company's mission of enhancing telehealth services.

The work culture for Data Engineers at Bask Health emphasizes collaboration, innovation, and a commitment to quality. Engineers often work closely with product teams and other departments, fostering an environment that encourages idea sharing and problem-solving. The company values growth and progressive thinking, making it an exciting place for Data Engineers to thrive and make tangible contributions to healthcare.

In answering this question, elaborate on specific projects where you designed and built data pipelines. Discuss the tools you used, the challenges you faced, and how you ensured data quality and performance. Highlight your problem-solving skills and willingness to learn from past experiences.

Provide a detailed description of an ETL process you've implemented, including the tools and technologies you used. Explain the steps involved, how you handled data extraction from various sources, and any challenges you overcame during the implementation phase.

Discuss practices you follow to ensure data integrity, such as data validation checks, routine audits, and error-handling measures. Mention how you communicate with other teams to maintain consistent data quality standards across the organization.

Share your hands-on experience with specific cloud services like AWS or GCP, focusing on how you've utilized these platforms to build or scale data solutions. Discuss your familiarity with services like S3, Redshift, or BigQuery and any relevant projects you've completed using these cloud tools.

Identify a particular data challenge you faced in your work and narrate how you approached the problem. Highlight your analytical thinking and the steps you took to reach a resolution, as well as the impact it had on your team or project outcomes.

In your response, share your methods for staying informed about the latest data engineering trends. Discuss resources like online courses, webinars, blogs, or participation in data engineering communities. Mention how you apply new knowledge to your work.

Explain your strategies for communicating technical concepts to non-technical teams. Highlight your experience in simplifying complex data topics, using analogies, or creating visual aids to bridge any knowledge gaps and facilitate better collaboration.

Discuss the techniques you employ for optimizing data pipeline performance, such as improving query efficiency, utilizing caching mechanisms, or parallel processing. Provide examples of how your optimizations have yielded significant performance improvements.

Outline the steps you take when creating data models, emphasizing your approach to understanding the business requirements before diving into technical details. Mention how you design schemas and any tools you use during the data modeling process.

Share your experience dealing with streaming data, including the frameworks you prefer to use. Explain how you'd approach integration with real-time data sources and the strategies you implement for processing and analyzing that data effectively.