Data Platform Engineer

A Data Platform Engineer at Kontakt.io is tasked with designing and building scalable data pipelines to process various datasets, including RTLS, IoT, and EHR data. This role involves implementing real-time event processing architectures with Kafka, optimizing ETL processes, and maintaining data lakes and warehouses for analytics and machine learning applications. Furthermore, you'll collaborate with software engineers and cloud teams to uphold a high-performing data platform and ensure compliance with HIPAA standards.

To excel as a Data Platform Engineer at Kontakt.io, you should have at least 5 years of experience in a similar role, working with big data frameworks like Apache Spark. Proficiency in Python, Scala, or Java is essential, alongside hands-on experience with streaming and batch processing using Kafka. Familiarity with cloud platforms like AWS and orchestration tools such as Docker and Kubernetes further enhances your fit for the position.

While the job location is not specified, Kontakt.io embraces modern work habits and is open to flexible work arrangements. This means that as a Data Platform Engineer, you may have the opportunity to work remotely, allowing you to collaborate with a diverse team and contribute to groundbreaking healthcare solutions from anywhere.

A Data Platform Engineer at Kontakt.io should be well-versed in big data technologies, primarily Apache Spark for data processing, and Kafka for stream and batch processing. Familiarity with cloud computing platforms like AWS, containerization tools like Docker, and orchestration frameworks such as Kubernetes will also be beneficial. Previous experience in healthcare systems, particularly various EHR systems, is a plus.

The culture at Kontakt.io is collaborative, innovative, and focused on transforming healthcare delivery through data. As a Data Platform Engineer, you will be part of a vibrant team dedicated to leveraging technology to improve patient care. Employees are encouraged to share ideas, take initiative, and partake in architectural discussions to ensure the continuous growth and efficiency of the data platform.

When answering this question, highlight specific projects where you utilized Apache Spark for data processing, detailing how you set up data pipelines, optimized performance, and resolved challenges you faced during the process.

Discuss the steps you've taken to secure sensitive healthcare data, including implementing role-based access control, encryption techniques, and ensuring data anonymization where necessary. Provide examples of how you have adhered to HIPAA standards in past roles.

Describe your familiarity with Kafka in terms of both batch and streaming data processing. Share specific examples of how you utilized Kafka in previous roles to handle real-time data ingestion and the impact it had on project success.

Discuss your approach to optimizing ETL processes. Mention the tools you’ve utilized, specific tuning techniques, and your steps in troubleshooting performance issues to enhance data processing efficiency.

Explain the specific AWS services you’ve used in your data engineering work, detailing how you utilized them in building scalable data architectures. Include experiences with services like AWS Lambda, S3, RDS, or Redshift that affirm your cloud proficiency.

Emphasize the importance of data governance in your previous projects. Discuss how you ensured data quality, implemented processes for data standardization, and collaborated with teams to maintain clean, reliable data for analysis.

Indicate the resources you use to keep your skills sharp and your knowledge current, such as online courses, industry conferences, tech blogs, or engagement in online communities focused on data engineering.

Share a specific example that showcases your problem-solving skills. Describe the challenge, your analysis of the situation, the approach you took to address it, and the successful outcome achieved.

Discuss the importance of collaboration in your work as a Data Platform Engineer. Provide examples of cross-team projects, emphasizing your communication skills and how you found solutions that met everyone's requirements.

Talk about your hands-on experience with EHR systems, including how you integrated and processed data from such systems, the challenges faced, and the impact that integration had on data-driven decision-making in a healthcare environment.