Data Engineer

Job Summary :

We are seeking an experienced Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data solutions. The ideal candidate will have strong expertise in Python, Apache Airflow, AWS, and Amazon Redshift, along with experience working on large-scale data engineering projects. This is a client-facing role requiring excellent communication skills and the ability to collaborate with cross-functional teams to deliver high-quality data solutions. Candidates with experience in Healthcare, Pharma, Life Sciences, or Clinical Data domains are preferred.

Job Description :

As a Data Engineer, you will be responsible for building and optimizing data pipelines, integrating data from multiple sources, and ensuring reliable data processing for analytics and reporting. You will work extensively with AWS services and Amazon Redshift to develop scalable data platforms while collaborating with business stakeholders and clients to understand data requirements and deliver robust data engineering solutions.

Key Responsibilities :

• Design,develop, and maintain scalable ETL/ELT data pipelines using Python and Apache Airflow.
• Build and manage cloud-based data engineering solutions using AWS services such as S3, Glue, Lambda, EMR, and Redshift.
• Develop,optimize, and maintain Amazon Redshift data warehouses for high-performance analytics.
• Integrate data from multiple structured and unstructured data sources.
• Perform data cleansing, transformation, and validation to ensure data quality and integrity.
• Optimize SQL queries, data models, and pipeline performance for large-scale datasets.
• Monitor, troubleshoot, and resolve issues related to data pipelines and workflows.
• Collaborate with clients, business stakeholders, data analysts, and engineering teams to gather requirements and deliver data solutions.
• Implement best practices for data governance, security, scalability, and reliability.
• Participate in code reviews, documentation, testing, and deployment activities.
• Support production environments by monitoring data workflows and resolving critical issues.
• Continuously improve existing data architecture and recommend enhancements to increase efficiency and performance.