Data Engineer

As a Data Engineer at System, your core responsibility will be designing and deploying scalable data pipelines that support machine learning and AI workflows, specifically integrating with EMR/EHR systems like Epic. You'll also research new tools for enhancing data infrastructure and collaborate with cross-functional teams to improve patient outcomes.

Ideal candidates for the Data Engineer role at System should have a minimum of 3 years of experience in supporting data-driven applications in production environments. Proficiency in SQL, experience with cloud-based data warehouses such as BigQuery or Snowflake, and familiarity with orchestration tools are essential qualifications. A solid understanding of statistical analysis is also beneficial.

At System, Data Engineers will work with a variety of tools and technologies. You’ll primarily use orchestration tools like Prefect or Airflow for data pipelines, cloud platforms like BigQuery or Redshift, and relational databases such as Postgres. Additionally, knowledge of deploying containerized applications with Kubernetes is helpful.

System is deeply committed to fostering a diverse and inclusive workplace. We believe that experiences, perspectives, and backgrounds enhance our work environment and improve our products. We ensure equal opportunity and accessibility in employment, encouraging all qualified applicants to apply.

The salary range for the Data Engineer position at System is between $145,000 and $190,000 per year, reflecting our commitment to pay equity and the valuable contributions our team members make to our mission.

When answering this question, focus on specific projects where you successfully designed and implemented data pipelines. Mention the tools and technologies you used, any challenges you faced, and how you ensured scalability and efficiency in your solutions.

Discuss your experience with orchestration tools like Prefect or Airflow, explaining how they help manage workflows. Highlight features such as scheduling, retries, and monitoring that make them essential for managing complex data pipelines.

Provide a structured response detailing your familiarity with EMR/EHR systems such as Epic. Discuss strategies for ensuring efficient data flow, managing extract-transform-load (ETL) processes, and maintaining data integrity during integration.

Share specific examples of challenges, such as performance issues or data migration complexities. Emphasize how you overcame these obstacles, showcasing your problem-solving skills and your ability to adapt in a cloud environment.

When responding, concentrate on a particular instance where you revamped data infrastructure. Explain the methods you employed, the impact of your improvements on data accessibility or processing speed, and any resulting benefits for the team or organization.

Articulate your strategies for ensuring data quality, such as implementing validation checks, error logging, and regular audits. Discuss how maintaining data accuracy is critical for successful machine learning and AI applications.

Highlight the importance of collaboration in your role. Share experiences where working alongside data scientists or software engineers led to successful project outcomes, emphasizing how effective communication drives results.

Discuss metrics such as throughput, latency, error rates, and resource utilization. Provide examples of how you’ve monitored these metrics to assess data pipeline performance and optimize them for better efficiency.

Share your methods for staying informed, whether it’s through tech blogs, online courses, webinars, or professional networks. Highlight your commitment to continuous learning in the evolving field of data engineering.

When answering this, align your skills and experiences with System's mission and values. Highlight your passion for using technology to improve healthcare outcomes and how your collaborative mindset fits within their team culture.