Data Engineer

A Data Engineer at Covera Health primarily focuses on designing, developing, and maintaining data pipelines that serve both internal and external sources. This includes creating real-time and scheduled workflows, transforming various datasets into unified data models, and collaborating with cross-functional teams to enhance business and product goals.

To qualify for the Data Engineer position at Covera Health, you should have 4+ years of experience in data engineering, proficiency in Python and SQL, and familiarity with cloud-managed systems like AWS or GCP. Experience with orchestration tools such as Airflow is also valuable, while experience in healthcare data environments is preferred.

Covera Health emphasizes ongoing growth by offering an annual professional development stipend, allowing Data Engineers to invest in courses, books, and activities that enhance their skills. This commitment to learning helps keep our Data Engineers at the forefront of industry advancements.

Yes, Covera Health is a remote-first company, welcoming Data Engineers from across the U.S. while offering a supportive work environment. Employees can choose to work in the NYC office for collaboration and connection, primarily on Wednesdays.

The salary for the Data Engineer position at Covera Health ranges from $165,000 to $180,000, complemented by a discretionary bonus and a comprehensive benefits package. Final compensation will depend on various factors like qualifications and experience.

When designing data pipelines, I start by understanding the business requirements, followed by assessing data sources, formats, and workflows. I prioritize creating reliable, scalable solutions and documenting each step to maintain high standards of data governance.

In my previous role, I worked extensively with big data systems, specifically focusing on data ingestion and management. I leveraged tools like Apache Spark and Hadoop, which I believe would be beneficial in optimizing the data pipelines at Covera Health.

I typically use Airflow for data orchestration due to its flexibility and strong support for managing complex workflows. Its ability to visualize DAGs helps in troubleshooting, making it easier to monitor pipeline performance.

I implement rigorous testing protocols during each stage of the pipeline development process. Regularly validating data, setting up alerts for anomalies, and adhering to data governance standards are essential to maintaining quality and integrity.

In a previous project, I encountered challenges with integrating disparate data systems. I worked with stakeholders to create a unified canonical data model, which streamlined analytics and resolved inconsistencies, ultimately improving data usability.

I adopt a test-and-learn approach, continuously monitoring pipeline performance metrics. Using profiling tools, I identify bottlenecks and optimize queries, adjust data loading strategies, and leverage caching wherever applicable.

I prioritize clear communication and documentation when collaborating with different teams. Regular check-ins and utilizing collaborative tools help ensure alignment on project goals and facilitate knowledge sharing across departments.

I have experience with visualization tools like Tableau, which I find essential for translating complex data insights into comprehensible formats for stakeholders. Strong visual representation of data aids in informed decision-making.

I was drawn to Covera Health’s mission of improving healthcare quality through data-driven insights. The opportunity to work on impactful projects and the chance to collaborate with industry leaders like Walmart and Microsoft aligns with my passion for data engineering.

I regularly attend webinars, participate in industry forums, and follow relevant publications. Engaging in professional networks allows me to stay informed about the latest trends, which I can apply to improve practices in my work.