Senior Data Engineer

Overview

We are looking for a Senior AWS Data Engineer to lead the design, development, and maintenance of scalable cloud-based data lakes, data warehouses, and analytics platforms. The ideal candidate will bring strong hands-on experience in AWS data services, ETL engineering, and data modeling, while collaborating closely with architects, analysts, DevOps teams, and business stakeholders.

This role requires strong ownership, problem-solving mindset, and the ability to work in complex, cross-functional environments—preferably with exposure to Healthcare, Life Sciences, Genomics, Clinical/Pre-clinical data domains.

Key Responsibilities

• Design, build, and optimize end-to-end data pipelines and ETL/ELT workflows on AWS.
• Lead development and maintenance of data lakes and enterprise data warehouses, supporting analytical and operational workloads.
• Implement scalable ingestion and integration frameworks for structured and semi-structured data from multiple sources.
• Develop and manage data processing pipelines using AWS Glue (PySpark), SQL, and Python frameworks.
• Own and improve data modeling and data warehouse design (dimensional models, star/snowflake schema).
• Ensure robust data quality checks, validation frameworks, monitoring, alerting, and logging for production reliability.
• Collaborate with cloud/platform/DevOps teams to implement CI/CD automation and Infrastructure-as-Code best practices where applicable.
• Implement and enforce data governance, security, and compliance across the AWS data ecosystem.
• Perform performance tuning and cost optimization across services like Redshift, Athena, Glue, and S3.
• Mentor junior engineers, conduct code reviews, enforce best practices, and contribute to technical standards.
  
  

• Strong hands-on experience with:
• Amazon S3, AWS Glue, Lake Formation, Redshift, Athena, Lambda
• Experience with Redshift Spectrum and partitioned querying best practices.
  
  

• Strong ETL development experience using:
• AWS Glue (PySpark), Python, and SQL
• Experience working with:
• CSV, JSON, Parquet, and API-based data ingestion
• Batch and near-real-time ingestion patterns
  
  

• Strong expertise in:
• Dimensional modeling
• Star and snowflake schema design
• Designing scalable and maintainable datasets for BI/analytics use
  
  

• Proven experience implementing:
• Data quality frameworks and automated validation checks
• Metadata management, lineage, and governance concepts
• Access controls and policy-driven governance (Lake Formation preferred)
  
  

• Hands-on experience with:
• GitHub
• CodeBuild / CodePipeline
• CI/CD practices for data workflows
  
  

• Strong understanding of:
• IAM, KMS, encryption, secure access patterns
• AWS cloud security best practices
  
  

• Bachelor’s/Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
• 4–8 years of experience in Data Engineering / Cloud Data Platforms.
• Exposure to Healthcare / Life Sciences / Pharma datasets and environments.
• Familiarity with compliance frameworks such as:
• GxP, HIPAA (good to have)
• Experience with visualization and analytics tools:
• QuickSight (optional)
• AWS Certifications (Good to have):
• AWS Certified Data Analytics – Specialty
• AWS Certified Developer – Associate
• AWS Certified Solutions Architect – Associate