Design, develop, and maintain data solutions for data generation, collection, and processing Be a key team member that assists in design and development of the data pipeline Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks Collaborate with multi-functional teams to understand data requirements and design solutions that meet business needs Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency Implement data security and privacy measures to protect sensitive data Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions Collaborate and communicate effectively with product teams Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions Adhere to standard methodologies for coding, testing, and designing reusable code/component Explore new tools and technologies that will help to improve ETL platform performance Participate in sprint planning meetings and provide estimations on technical implementation What we expect of you We are all different, yet we all use our unique contributions to serve patients. Basic Qualifications: Master s degree and 1 to 3 years of Computer Science, IT or related field experience OR Bachelor s degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Preferred Qualifications: Functional Skills: Must-Have Skills Proficiency in Python, PySpark, and Scala for data processing and ETL (Extract, Transform, Load) workflows, with hands-on experience in using Databricks for building ETL pipelines and handling big data processing Experience with data warehousing platforms such as Amazon Redshift, or Snowflake. Strong knowledge of SQL and experience with relational (e.g., PostgreSQL, MySQL) databases. Familiarity with big data frameworks like Apache Hadoop, Spark, and Kafka for handling large datasets. Experienced with software engineering best-practices, including but not limited to version control (GitLab, Subversion, etc.), CI/CD (Jenkins, GITLab etc.), automated unit testing, and Dev Ops Good-to-Have Skills: Experience with cloud platforms such as AWS particularly in data services (e.g., EKS, EC2, S3, EMR, RDS, Redshift/Spectrum, Lambda, Glue, Athena) Strong understanding of data modeling, data warehousing, and data integration concepts Understanding of machine learning pipelines and frameworks for ML/AI models Professional Certifications: AWS Certified Data Engineer (preferred) Databricks Certified (preferred) Soft Skills: Excellent critical-thinking and problem-solving skills Strong communication and collaboration skills Demonstrated awareness of how to function in a team setting Demonstrated presentation skills