Data Engineer Healthcare and Life Sciences

Role Name : Data Engineer Healthcare and Life Science

Domain : Information Technology and Healthcare

Role Type : Full Time

Location : Washington DC USA

Experience: 10 -15 Years

Role Overview: The Technical Consultant role is responsible for end-to-end client management program management business growth and client success by ensuring solutions are scalable secure cost-efficient and aligned with modern data engineering analytics and AI/ML best practices. The consultant will partner with engineering teams data product owners and business stakeholders to establish architectural standards design cloud-native data platforms and guide technical execution across complex programs.

Roles and Responsibilities:

• Drive senior client workshops problem framing and solution framing
• Lead end-to-end client advisory opportunity creation and demand generation
• Engage with business and technical teams to align architecture with business requirements
• Support RFP and RFI responses and architectural evaluations
• Experience with Life Sciences datasets migration and modernization
• Understanding of operational layers L1 L2 and L3 along with ontology and context layers
• Define and enforce data modeling metadata lineage and quality standards
• Implement CI/CD pipelines and monitoring frameworks
• Ensure architectures support observability reliability and operational excellence
• Architect end-to-end solutions on AWS and Databricks
• Define architectural standards patterns and best practices
• Review and guide solution designs for scalability performance and security
• Evaluate technology options and recommend long-term architectural strategies
• Translate business and analytical requirements into scalable data models
• Assess current-state data architectures and define future-state models
• Design scalable data models for analytical and operational workloads
• Demonstrate strong understanding of metadata lineage data quality and governance frameworks
• Be familiar with distributed compute paradigms and cloud-native modeling patterns

Required Qualifications:

• At least 10 years of experience in AI software development data engineering or data architecture along with
• 5 years of Life Sciences consulting experience
• Experience driving modernization initiatives for AI products with AI foundations governance operational layers and context layers
• Strong experience with Databricks DBT Core or Cloud Python Spark SQL distributed compute paradigms including in-memory distributed and MPP architectures and Data Vault 2.0 including automatedvDeep knowledge of AWS services including S3 Glue Redshift EMR DynamoDB Lambda Athena and Kinesis
• Experience leading architecture across multi-team onsite and offshore delivery models
• Executive presence to lead VP and Executive Director-level meetings and steering committees independently
• Ability to drive thought leadership technical visioning workshops and client success initiatives
• Life Sciences experience preferred
• Ability to translate complex analytical requirements into scalable architectures including data models ETL pipelines and consumption layers
• Experience designing and deploying dashboards and self-service analytics on relational and non-relational databases
• Strong understanding of CI/CD DevOps static code analysis and test-driven development
• Experience with cloud migration patterns and modern data platform design
• Experience defining data standards metadata models lineage quality rules and governance patterns
• Experience implementing logging monitoring observability and cost optimization frameworks
• Experience driving enterprise data platform adoption and modern data practices