Jr Data Engineer

SmartLight is building out its next-generation Snowflake data warehouse to power claims analytics waste and abuse analytics and client reporting at scale. Were 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: youll 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.

• Design build and maintain data ingestion pipelines feeding SmartLights 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 SmartLights change management SDLC and data security practices given the sensitivity of the healthcare data we handle

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

• 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 teams 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 SmartLights 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