Associate Data Engineer

• 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 cross-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
• Identify and resolve complex data-related challenges
• Adhere to best practices 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

We are all different, yet we all use our unique contributions to serve patients.

Basic Qualifications:

• Bachelors degree and 0 to 3 years of Computer Science, IT or related field experience OR
• Diploma and 4 to 7 years of Computer Science, IT or related field experience

Preferred Qualifications:

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
• 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.

• Experience with cloud platforms such as AWS particularly in data services (e.g., EKS, EC2, S3, EMR, RDS, Redshift/Spectrum, Lambda, Glue, Athena)
• Understanding of data modeling, data warehousing, and data integration concepts
• Understanding of machine learning pipelines and frameworks for ML/AI models

Professional Certifications (please mention if the certification is preferred or mandatory for the role):

• AWS Certified Data Engineer (preferred)
• Databricks Certified (preferred)

• 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