What are the responsibilities and job description for the Data Engineer - Healthcare and Life Sciences position at YO AI Labs?
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