We are seeking a Senior Data Engineer with deep expertise in data warehousing, ETL pipeline development, and Snowflake to lead the modernization of our data infrastructure. In this role, you will build new data pipelines and warehouse models in Snowflake while partnering with our existing SQL Server Data Engineering team to migrate legacy systems to the cloud. You will work with healthcare claims, eligibility, and related data ingested from carrier and TPA partners, as well as data aggregated from internal systems. This is a hands-on technical role requiring strong self-management, a proven ability to mentor peers, and comfort working with sensitive healthcare data under strict security requirements. As a scaling technology organization, we value ownership, collaboration, and continuous improvement — you will have a direct hand in shaping the tools, processes, and architecture that power our data platform. Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.Key Responsibilities
Design, build, and maintain ETL and reverse-ETL pipelines between Snowflake, Azure Data Factory, and legacy SQL Server systems.
Develop and optimize Snowflake data warehouse models, ensuring performance, reliability, and high availability.
Implement and maintain row-level and column-level security policies to protect sensitive healthcare data.
Partner with the existing SQL Server Data Engineering team to plan and execute the migration of legacy ETL processes and warehouse models to Snowflake and the cloud.
Build and maintain data transformations using dbt.
Monitor pipeline health, troubleshoot failures, and ensure uptime and data integrity across all data flows.
Mentor peers and contribute to engineering best practices, code reviews, and documentation.
Learn and adopt Sigma as the organization’s BI and reporting tool. Required Skills and Qualifications
Bachelor’s degree in Computer Science, Data Engineering, or related field, or equivalent experience.
10+ years of experience in data engineering, ETL development, and data warehouse modeling.
Proven hands-on experience with Snowflake, including architecture, optimization, and security.
Strong proficiency in SQL and Python.
Experience with at least one major BI/visualization platform (e.g., Power BI, Tableau, Looker, Sigma).
Experience with dbt and Azure Data Factory.
Prior experience working with healthcare data, including familiarity with data sensitivity, HIPAA, and security best practices.
Experience implementing row-level and/or column-level security in a data warehouse environment.
Strong self-management skills with the ability to work independently in a fully remote environment. Preferred Qualifications
Experience with healthcare claims, eligibility, or related payer/TPA data.
Experience migrating data infrastructure from SQL Server to Snowflake or other cloud platforms.
Familiarity with Sigma.
Experience working in Agile development environments with tools such as Jira.
Prior experience in a startup or fast-growing technology company. About SmartLight Analytics
SmartLight Analytics was formed by a group of industry insiders who sought to reduce rising healthcare costs for self-funded employers. Through proprietary data analysis, SmartLight identifies and mitigates wasteful healthcare spending without disrupting employee benefits or requiring behavior changes.
SmartLight Analytics is a healthcare technology company founded by industry insiders dedicated to addressing the rising costs of healthcare. The company combats fraud, waste, and abuse in healthcare through proprietary data analysis and advanced model development. SmartLight specializes in healthcare payment integrity, offering solutions that leverage machine learning and analytics to improve cost efficiency, care quality, and operational performance across the healthcare ecosystem. The company serves health plan sponsors and payers with data-driven insights for claims analysis, risk adjustment, provider performance optimization, and utilization forecasting.
