• Develop and maintain data pipelines between disparate data sources. • Extract, Transform, and Load data utilizing efficient SQL queries to meet end user needs. • Collaborate with peers and end users to provide novel technical solutions to unique their business problems. • Build low maintenance automations to standardize our business processes. • Conduct in depth ad hoc analysis and reporting to support business needs. • Ensure confidentiality of patient information and other sensitive data by adhering to HIPAA compliance requirements.

• Build and maintain data lake and data warehousing solutions for a B2B multi-tenant SaaS solution • Participate in the entire data pipeline lifecycle • Lead code and design reviews with junior and peer developers • Write clean code to develop data pipelines and necessary infrastructure as code • Monitor and troubleshoot performance, scalability, and security issues, making improvements as needed • Participate in the evaluation of new technologies and tools that can improve performance of our system • Participate in data governance process • Contribute to the design and documentation of our data lake platform • Gather technical and design requirements • Communicate effectively with technical and non-technical team members to ensure alignment and successful execution of projects

• Create the conceptual, logical and physical data models. • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of sources like Hadoop, Spark, AWS Lambda, etc. • Lead and/or mentor a small team of data engineers. • Design, develop, test, deploy, maintain and improve data integration pipelines. • Develop pipeline objects using Apache Spark / Pyspark / Python or Scala. • Design and develop data pipeline architectures using Hadoop, Spark and related AWS Services. • Load and performance test data pipelines built using the above-mentioned technologies. • Communicate effectively with client leadership and business stakeholders. • Participate in proposal and/or SOW development

• Develop and maintain MLOps pipelines to automate the lifecycle of machine learning models. • Implement and optimize CI/CD pipelines specific to machine learning workflows. • Continuously monitor model performance in production, including logs, metrics, and alerts. • Design and apply strategies to detect and mitigate model performance degradation. • Ensure the security, governance, and compliance of data and models in production environments. • Collaborate with multidisciplinary teams to document and standardize MLOps-related processes.