Senior Data Engineer 1

As a Senior Data Engineer 1 at Modernizing Medicine, your primary responsibilities include developing and maintaining reliable data pipelines, guiding data infrastructure design, and collaborating with engineering and product teams to create new data products. You'll also maintain documentation for the Data Warehouse and assist in mentoring junior engineers. Your role demands strong problem-solving skills and a commitment to quality throughout diverse data engineering projects.

To qualify for the Senior Data Engineer 1 role at ModMed, candidates should possess a master's degree in Computer Science or equivalent experience, alongside a strong knowledge of SQL, Spark, and data warehouse design principles. Experience with AWS services, as well as familiarity with data pipeline technologies such as Airflow or Kafka, is recommended. A commitment to quality and strong problem-solving skills are also essential for success in this position.

The Senior Data Engineer 1 at Modernizing Medicine significantly contributes to healthcare technology by building the infrastructure that supports data analysis and insight generation. By creating robust data pipelines and collaborating with data scientists and engineers, you empower teams to harness data for improved healthcare outcomes, ultimately enhancing the experience for doctors and their patients.

In the role of Senior Data Engineer 1 at Modernizing Medicine, you will have the opportunity to work with a variety of cutting-edge technologies, including SQL, Spark/pySpark, AWS services like S3 and EMR, as well as data pipeline frameworks such as Airflow and streaming platforms like Kafka. Familiarity with business intelligence tools and data transformation technologies like Databricks will also be beneficial.

The team culture at ModMed for the Senior Data Engineer 1 role is rooted in collaboration, innovation, and a positive atmosphere. We encourage open exchanges of ideas and viewpoints, ensuring that every team member's contribution is valued. You'll find a supportive environment where passion for technology meets a commitment to making a difference in healthcare.

Discuss your specific experiences by highlighting projects where you designed and implemented data pipelines. Emphasize the technologies you used, the challenges you faced, and how you optimized for reliability and performance. Providing metrics or outcomes can also demonstrate your impact in previous roles.

Focus on your methodologies for quality assurance, such as implementing automated testing, documenting processes, and conducting code reviews. Mention how you monitor data pipelines to address issues proactively, thereby ensuring data accuracy and reliability.

Outline your proficiency with key tools, such as SQL for database management, Spark for big data processing, and any experience with cloud services like AWS. It’s also effective to discuss your familiarity with data orchestration tools like Airflow and your ability to adapt to new technologies as needed.

Share examples of how you've effectively worked with product teams, data analysts, and other engineers to develop data-driven solutions. Highlight your communication skills and how you ensure alignment between technical and business goals.

Provide specific examples of challenges, such as scalability issues or integrating disparate data sources. Describe the steps you took to resolve these issues and the lessons you learned, showcasing your problem-solving abilities.

Discuss the role of data warehousing in consolidating data from various sources and how structured designs like star schemas can optimize query performance and reporting. Sharing insights on your hands-on experience with designing data warehouses can illustrate your understanding.

Emphasize that data engineers provide the foundational infrastructure that enables data analysis and decision-making. You are the bridge between data generation and data consumption, ensuring that data is accessible, reliable, and ready for insights.

Mention your commitment to continuous learning through online courses, tech meetups, conferences, and industry publications. Highlight any specific resources or communities that help you remain informed about emerging trends in data engineering.

Share what excites you about data engineering, whether it's problem-solving, working with innovative technologies, or contributing to impactful projects in sectors like healthcare. Your passion can resonate with the interviewers.

Discuss your approach to mentorship, focusing on providing constructive feedback, encouraging best practices, and facilitating knowledge-sharing. Highlight the importance of creating a supportive environment for learning and growth.