About the role
SmartLight is building out its next-generation Snowflake data warehouse to power claims analytics, waste and abuse analytics, and client reporting at scale. We're looking for a Junior Data Engineer to join our Data Platform Team and grow alongside a modern, AI-augmented data stack. This is a hands-on role: you'll ingest and model real healthcare claims data, build transformation pipelines in dbt, and help shape the fact/dimensional models and semantic layer that our analysts and reporting tools depend on every day.
This role suits someone early in their data engineering career who has strong fundamentals and is comfortable working independently once given clear requirements — not someone who needs each task broken into small steps.
What You'll Do
Design, build, and maintain data ingestion pipelines feeding SmartLight's Snowflake warehouse from claims, eligibility, and other healthcare data sources
Develop and maintain transformation models in dbt, following testing, documentation, and version-control best practices
Contribute to fact and dimensional modeling (star schema design, slowly changing dimensions, grain definition) supporting claims analytics use cases
Support and help maintain a semantic layer that gives consistent, governed metrics to downstream reporting tools (e.g., Sigma)
Troubleshoot data quality issues, pipeline failures, and schema drift with minimal escalation
Write idempotent, reliable pipeline logic that can be safely rerun without creating duplicate or inconsistent data
Collaborate with senior data engineers, analysts, and product stakeholders to translate business/reporting requirements into technical data structures
Use AI-assisted development tools (Claude, Copilot, or similar) as a core part of your daily workflow — for code generation, debugging, documentation, and accelerating pipeline development — while maintaining human review and code quality standards
Follow SmartLight's change management, SDLC, and data security practices, given the sensitivity of the healthcare data we handle
What You'll Need
Required:
Solid foundational knowledge of data engineering: ETL/ELT concepts, SQL proficiency, and data pipeline design
Working knowledge of dbt (or strong readiness to ramp quickly if exposure is limited) for transformation and modeling
Understanding of fact and dimensional modeling principles (star/snowflake schemas, grain, SCDs)
Familiarity with the concept of a semantic layer and why it matters for consistent, trustworthy reporting
Strong data translation fundamentals — the ability to take a business question or reporting requirement and reason through the correct data structure/logic to answer it accurately
Ability to work independently and complete assigned tasks with minimal day-to-day supervision once requirements are clear
Comfort using AI tools as a core part of the development process — this is a non-negotiable expectation of how we build, not an optional add-on
Strong written communication skills for documentation and cross-team collaboration
Preferred:
Prior experience in healthcare data (claims, eligibility, EHR, or similar) — familiarity with concepts like UB-04 revenue codes, claims adjudication, or payer/provider data structures is a plus
Experience with Snowflake specifically
Exposure to Terraform or other infrastructure-as-code practices
Familiarity with Sigma, Looker, Power BI, or other modern BI/reporting tools
Understanding of HIPAA-related data handling considerations
What Success Looks Like
You can take a data ingestion or modeling task, ask clarifying questions up front, and deliver a working, tested solution without needing hand-holding through implementation
Your dbt models are well-documented, tested, and follow the team's established modeling conventions
You proactively flag data quality issues or schema risks before they become downstream reporting problems
You use AI tools fluently to move faster without sacrificing code quality, security, or accuracy — especially given SmartLight's obligations around GenAI use disclosure in some client contracts
You grow into increasing ownership of the Snowflake buildout over time, with a path toward more senior data engineering responsibilities
Originally posted on Himalayas