Description

You will lead the design, development, and implementation of scalable data architectures and pipelines to support analytics and machine learning.

Responsibilities

• Design and implement scalable data pipelines and architectures for business intelligence and machine learning.
• Optimize ETL/ELT processes to improve data reliability, efficiency, and scalability.
• Collaborate with data scientists, analysts, and software engineers to ensure data integration and accessibility.
• Mentor junior data engineers and manage day-to-day work assignments and project deliverables.
• Ensure data governance, security, and compliance with industry standards.
• Troubleshoot data-related issues to maintain system stability and performance.

Required Skills

• 7–10 years of experience in data engineering, including at least 2 years in the healthcare domain.
• Master's degree in Computer Science or a related discipline.
• Expertise in SQL, Python, and Spark for data processing and transformation.
• Hands-on experience with cloud platforms including AWS, Azure, or GCP.
• Deep knowledge of ETL/ELT pipelines, data warehousing, and real-time data streaming.
• Experience with big data technologies such as Hadoop, Apache Kafka, or Snowflake.
• Strong understanding of data modelling, database optimization, and performance tuning.
• Knowledge of CI/CD pipelines and DevOps practices for data infrastructure.
• Familiarity with Electronic Health Records (EHR), wearable devices, and sensor data.
• Knowledge of healthcare data compliance such as HIPAA and GDPR.

Key Skills

Education

Any Graduate