Data Engineer - Healthcare and Life Sciences

YO AI Labs; Washington (District of Columbia); On-site; USD 140,000 - 210,000; Full time

Job summary

YO AI Labs is seeking an experienced Data Engineer for Healthcare and Life Science domains in Washington DC. You will architect cloud-native data platforms on AWS and Databricks, define data models, metadata, lineage and quality standards, and ensure observability, reliability, and cost efficiency across analytics and operations.

The role requires partnering with engineering teams, data product owners, and senior stakeholders to drive solution framing, workshops, RFP responses, and client

Qualifications

• 10+ years in AI, software development, data engineering, or data architecture.
• 5+ years Life Sciences consulting experience.
• Experience modernizing AI products with governance and data layers.
• Proficiency with Databricks, Python, Spark, SQL, and AWS services.
• Experience leading multi-team onsite and offshore delivery.
• Executive presence to lead VP/Director-level meetings.
• Translate analytics requirements into scalable architectures and data models.
• Experience designing dashboards and analytics on relational/non-relational databases.
• Knowledge of CI/CD, DevOps, and modern data platform patterns.
• Experience cloud migration and data governance patterns.

Responsibilities

• Lead senior client workshops and solution framing.
• Drive end-to-end advisory, opportunity creation, and demand generation.
• Align architecture with business requirements across teams.
• Support RFP/RFI responses and architectural evaluations.
• Define data models, metadata, lineage, and quality standards.
• Architect end-to-end solutions on AWS and Databricks.
• Review designs for scalability, performance, and security.
• Guide long-term architectural strategies.
• Translate requirements into scalable data models and ETL pipelines.
• Oversee data platform modernization and governance.

Skills

Executive presence

Client leadership

Architectural thinking

Problem framing

Cross-team collaboration

Tools

Databricks

DBT Core/Cloud

Python

Spark

SQL

AWS

Data Vault 2.0

S3

Glue

Redshift

EMR

Kinesis

DynamoDB

Lambda

CI/CD

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

The Technical Consultantrole 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 least10+ 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