• Design, build, and maintain scalable data pipelines and ELT/ETL workflows that support analytics, operational reporting, and business intelligence use cases • Build programmatic data pipelines (primarily in Python) that extract data from application and third-party systems, transform it into usable formats, and deliver it to downstream data platforms and consumers • Own and improve core data models and transformations to ensure data is accurate, well-structured, and easy for stakeholders to use • Partner with Product, Engineering, and Analytics teams to understand data needs and translate them into reliable data solutions • Develop and maintain systems that move data across the platform, ensuring it is properly shaped, structured, and available for downstream analysis and product use cases • Help shape and maintain the architecture of MDCalc’s modern data stack, including warehousing, orchestration, transformation, and monitoring • Improve data quality, observability, and reliability through testing, validation, and proactive monitoring practices • Support the ingestion and integration of data from a variety of application, product, and third-party sources • Establish and reinforce best practices around data governance, documentation, naming conventions, and maintainability • Identify and drive opportunities to improve performance, scalability, and efficiency across our data systems • Design efficient data workflows that query, transform, and deliver datasets to downstream systems and stakeholders • Contribute to technical direction and architectural decisions as a senior member of the team • Serve as a thought partner to teammates and cross-functional stakeholders on how to best leverage data across the business

• Develop, operate, and troubleshoot data pipelines in production environments, ensuring correct execution of data loads; • Work on data modeling and create Power BI dashboards, following performance and governance best practices; • Develop and optimize complex SQL queries, handling large volumes of data; • Monitor and maintain pipelines and workloads in Databricks; • Respond to critical support incidents, investigate issues, and ensure stability and continuity of deliveries during the night shift; • Identify and fix failures in data ingestion, transformation, and delivery processes; • 8-hour workday, Sunday to Thursday (10:00 PM to 6:00 AM), with weekend on-call duty.

• Support and protect critical enterprise cybersecurity data environments • Design and integrate secure data pipelines, log management systems, and endpoint protection platforms across hybrid infrastructures • Work at the intersection of security engineering, data architecture, and compliance • Ensure scalable, resilient, and secure data solutions aligned with federal requirements

• Develop, maintain, and optimize data pipelines for analytics and machine learning workloads. • Ensure reliability, scalability, and performance of data workflows. • Implement data ingestion from internal and external sources. • Collaborate with Data Scientists and Analysts to prepare datasets for modeling and analysis. • Build and maintain Databricks-based data pipelines and workflows. • Monitor, debug, and improve data pipeline performance and platform efficiency. • Develop tools and frameworks to automate data operations and ensure data quality. • Work with product and engineering teams to translate data requirements into solutions. • Participate in code reviews and follow data engineering best practices. • Maintain documentation and contribute to improving data engineering standards and tooling.