Data Engineer - Healthcare and Life Sciences

Job Description

Job Description

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 automate_dvDeep 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