Data Solutions Engineer

About this role

GovWell seeks a Data Solutions Engineer to own end-to-end data migrations for government agencies transitioning to their AI-powered operating system. You'll transform messy legacy data into clean, production-ready datasets while collaborating with non-technical government staff and internal teams to ensure accurate onboarding and identify data quality issues before they impact clients.

What you'll do

• Manage complete data migration workflows from legacy systems to GovWell, including schema mapping, validation, and dry-runs
• Identify data quality issues, inconsistencies, and edge cases in government databases before production deployment
• Lead technical calls with government staff to explain data requirements and guide system transitions
• Build and improve migration processes, templates, and playbooks to accelerate onboarding
• Document recurring migration challenges and feedback to inform product and platform improvements
• Write production SQL and Python code for data validation, reconciliation, and transformation

What they're looking for

• Python (reading, debugging, and shipping production code)
• SQL (analysis, validation, reconciliation, transformation)
• Data migrations and ETL/ELT processes
• Data quality and validation frameworks
• Technical communication with non-technical stakeholders
• Geospatial data handling
• Schema mapping and database design
• Problem-solving with messy, undocumented legacy systems

Benefits

• Direct reporting to Co-Founder & CTO Ben Cohen
• Hybrid work (3+ days per week in NYC office)
• Early-stage startup with 4X ARR growth and strong product-market fit
• High-impact work affecting millions of residents across 35+ states
• Cross-functional collaboration with Deployment, Product, and Engineering
• Exceptional autonomy and ownership over onboarding process evolution

Likely interview questions

• Walk us through your most complex data migration project—what made it challenging and how did you ensure data quality?
• Tell us about a time you discovered a critical data issue late in a migration. How did you handle it and what did you learn?