Lead Engineer, Analytics Platform & AI
Huron Consulting · Remote · Chicago, United States · 1d ago
Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We help healthcare organizations build innovation capabilities and accelerate key growth initiatives, enabling organizations to own the future, instead of being disrupted by it. Together, we empower clients to create sustainable growth, optimize internal processes and deliver better consumer outcomes.
Health systems, hospitals and medical clinics are under immense pressure to improve clinical outcomes and reduce the cost of providing patient care. Investing in new partnerships, clinical services and technology is not enough to create meaningful and substantive change. To succeed long-term, healthcare organizations must empower leaders, clinicians, employees, affiliates and communities to build cultures that foster innovation to achieve the best outcomes for patients.
Joining the Huron team means you’ll help our clients evolve and adapt to the rapidly changing healthcare environment and optimize existing business operations, improve clinical outcomes, create a more consumer-centric healthcare experience, and drive physician, patient and employee engagement across the enterprise.
Join our team as the expert you are now and create your future.
This is a hands on leadership role. You will write and review code, set architecture and technical standards, and lead a team of engineers, while partnering closely with product management, data engineering, and data science to bring intelligent, AI enabled analytics capabilities to market faster.
Responsibilities
Full stack product development
- Lead design and development across the stack: responsive front end interfaces, APIs and services, and the underlying data layer for analytics products.
- Set technical direction and architecture for scalability, security, and performance of AWS based analytics solutions.
- Collaborate with product management on roadmap, prioritization, and feature delivery using Agile practices.
AI and machine learning in analytics
- Identify and lead opportunities to embed AI and Agentic AI capabilities into analytics products: predictive models, natural language interfaces over data, automated insight generation, and generative AI features that help clients act faster on their data.
- Partner with data scientists to move machine learning models from prototype into reliable, production grade services.
- Evaluate and apply large language model based tools where they add measurable client value, with attention to data privacy, accuracy, and healthcare compliance requirements.
Data engineering
- Design and maintain ETL and data pipeline processes that feed dashboards, models, and client facing reporting.
- Work with cloud data services, including S3, Redshift, Glue, Lambda, and Athena, to build scalable data infrastructure.
- Uphold data governance, quality, and healthcare data handling standards across the platform.
Team leadership
- Support, and mentor a team of engineers, providing technical guidance and career development.
- Establish and continuously improve engineering practices, code quality standards, and delivery processes.
- Communicate progress, risks, and technical decisions clearly to product leadership and cross functional stakeholders.
Qualifications
Required:
- 8 or more years of professional software engineering experience, including full stack development across both front end and back end technologies.
- 3 or more years leading or mentoring engineers, formally or informally.
- Demonstrated experience applying AI or machine learning within a production analytics or data product, such as predictive modeling, recommendation systems, or generative AI features.
- Strong front end development skills in TypeScript, JavaScript, and React.
- Strong back end skills in Python, and an equivalent language used for analytics and API development.
- Solid understanding of relational databases and SQL.
- Working knowledge of core AWS services relevant to data and analytics, including Lambda, IAM, S3, Redshift, Glue, and Athena.
- Experience with infrastructure as code using AWS CloudFormation and the AWS CDK.
- Experience with version control systems, particularly Git.
- Experience delivering software using Agile methodologies.
- Excellent written and verbal communication skills, with the ability to explain technical tradeoffs to non-technical stakeholders.
Preferred:
- Familiarity with healthcare data interoperability standards, such as ANSI X12, HL7, and FHIR.
- Experience with healthcare claims data is a plus.
- Hands on experience with large language models, prompt engineering, retrieval augmented generation, or vector databases.
- Experience with MLOps practices for deploying and monitoring machine learning models in production.
- Experience with SQLAlchemy and PostgreSQL.
- Experience with row based databases such as Amazon RDS and Aurora.
- Experience with columnar or data warehouse platforms such as Redshift, Athena, and Snowflake.
- Experience with NoSQL databases such as DynamoDB.
- Experience with a broader range of AWS services beyond the core set, and with Azure DevOps for build and release pipelines.
- Experience with big data technologies such as Spark, or infrastructure as code tools such as Terraform.
- Background in healthcare or higher education analytics.
- AWS certification, such as AWS Certified Solutions Architect or AWS Certified Developer.
- A bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.