Description

What You'll Do:

• Design, develop, and maintain scalable data pipelines and ETL/ELT solutions using modern data engineering practices.
• Build and optimize data models to support reporting, analytics, machine learning, and AI workloads.
• Develop and manage large-scale batch and real-time data processing solutions using Spark and Kafka/Kinesis.
• Work extensively with AWS services including Glue, Redshift, Athena, and S3 to build cloud-native data solutions.
• Implement and optimize data integration, transformation, and orchestration workflows.
• Leverage Databricks to build scalable data engineering and analytics platforms.
• Ensure data quality, governance, security, and compliance standards across enterprise data ecosystems.
• Collaborate with business stakeholders, architects, analysts, and developers to translate business requirements into technical solutions.
• Design and implement healthcare data integration and interoperability solutions using FHIR standards.
• Monitor and optimize data pipeline performance, scalability, and reliability.
• Support data migration, modernization, and cloud transformation initiatives.
• Implement best practices for data architecture, metadata management, and data lifecycle management.
• Contribute to code reviews, technical design discussions, and engineering excellence initiatives.
• Mentor junior engineers and promote data engineering best practices across teams.
• Utilize GitHub Copilot, Microsoft 365 Copilot, and approved AI tools to improve engineering productivity and software quality.
• Stay current with emerging AI technologies, data engineering trends, and industry best practices.

Expertise You'll Bring:

• 8 to 12 years of experience in Data Engineering, Data Warehousing, and Big Data technologies.
• Strong hands-on experience with Python, SQL, Spark, and Databricks.
• Expertise in designing and developing enterprise-scale ETL/ELT data pipelines.
• Strong experience in Data Modeling, Data Architecture, and Data Warehousing concepts.
• Hands-on experience with AWS Glue, Redshift, Athena, and S3.
• Expertise in streaming technologies such as Kafka and Kinesis.
• Strong understanding of data governance, data quality, lineage, and compliance frameworks.
• Experience building scalable batch and real-time processing architectures.
• Strong performance tuning and optimization experience for Spark and SQL workloads.
• Experience implementing CI/CD practices for data engineering projects.
• Knowledge of healthcare data standards, particularly FHIR (Fast Healthcare Interoperability Resources).
• Experience supporting analytics, reporting, AI, and machine learning data platforms

Key Skills

Education

Any Graduate