Note: The job is a remote job and is open to candidates in USA. SmartLight Analytics is building a next-generation Snowflake data warehouse to support healthcare claims analytics, waste and abuse analytics, and client reporting. The Junior Data Engineer will ingest and model healthcare claims data, develop dbt transformation pipelines, support dimensional models and semantic layers, and troubleshoot data quality and pipeline issues using AI-assisted development tools.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- You grow into increasing ownership of the Snowflake buildout over time, with a path toward more senior data engineering responsibilities
Company Overview
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