Manager, Data Engineering

ModMed is hiring a driven Data Engineering Manager with a deep passion for technology and an expert grasp of PySpark, SQL, Databricks, AWS and Airflow. This pivotal role focuses on leading our efforts in managing and building complex data pipelines and implementing strategies to enhance data reliability and minimize redundancies. Positioned at the core of our strategic initiatives, the Data Engineering Manager will drive innovation and enhance our capabilities in big data and cloud technologies within a fast-paced Healthcare IT company that is truly Modernizing Medicine!

Your Role

• Oversee a team of data engineers to enhance and maintain our big data platform, ensuring scalability, performance, and reliability.
• Architect and implement sophisticated big data processing pipelines using PySpark and Databricks, with an emphasis on optimizing data throughput and processing capabilities.
• Leverage Airflow for the automation and orchestration of complex data workflows, maintaining data integrity and availability across systems.
• Work closely with product management and customer experience teams to design, develop, and deploy intuitive BI solutions that meet the needs of our customers, enhancing user engagement and satisfaction.
• Promote a culture of innovation and continuous learning, keeping abreast with the latest trends and advancements in big data, analytics, and cloud technologies.
• Ensure adherence to data governance, security, and compliance frameworks across all big data and analytics platforms, with a special focus on AWS and Databricks security practices.
  
  

Skills & Requirements

• 7 years of relevant technical work experience in data engineering, platform engineering, or related fields, with deep expertise in designing and building distributed data systems and large-scale data warehouses
• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, with a preference for an advanced degree.
• Extensive experience in a leadership role within the field of data analytics or engineering, with a demonstrated proficiency in PySpark, SQL, Databricks, AWS and Airflow.
• Expert knowledge of big data architectures and processing techniques, with hands-on experience in managing large-scale data systems.
• Proficiency in SQL, alongside experience with both relational and non-relational databases.
• Demonstrated analytical and problem-solving abilities, capable of addressing and simplifying complex business challenges.
• Strong leadership skills, with a history of mentoring and guiding high-performing data engineering teams.
• Deep understanding of cloud infrastructure, preferably AWS, including best practices for security, scalability, and efficiency.