Data Solutions Engineer

Responsibilities

• Own data migrations end-to-end: work directly with government staff to understand source systems, configure schema mappings and ingestion settings, run validations and dry-runs, ensure accurate and complete data lands in GovWell.
• Act as gatekeeper for data quality: identify gaps, inconsistencies, and edge cases in legacy data; make documented judgment calls within constraints; prevent bad or unsafe data from reaching production.
• Serve as technical face of onboarding: lead customer calls, explain data requirements and tradeoffs, guide agency staff through transition from legacy systems.
• Improve the migration process: use and enhance internal tools, templates, and playbooks to reduce time-to-launch and minimize manual work.
• Close feedback loop with Product and Engineering: document recurring issues, unmet assumptions, and tooling gaps to help evolve the platform.

Requirements

• 2-4 years of hands-on experience in data migrations, ETL/ELT, technical implementations, or closely related role working with messy, real-world production data.
• Demonstrated ability to work directly in a Python codebase day-to-day (reading, debugging, and shipping changes via PRs), ideally in a data/pipeline repo (e.g., Airflow DAGs, ingestion jobs, migration utilities).
• Strong SQL proficiency for analysis, validation, reconciliation, and transformation.
• Experience translating ambiguous stakeholder requirements into concrete, testable migration/implementation plans.
• Track record of operating in execution-heavy environments with high ownership and high attention to correctness.

Nice-to-Haves

• Experience with workflow orchestration and pipeline operations (Airflow, Dagster, Prefect, etc.), including interpreting logs, safe reruns, and handling large backfills/imports.
• Experience with customer-facing implementation/onboarding work, especially explaining constraints and tradeoffs to non-technical stakeholders.
• Familiarity with geospatial data concepts or GIS tooling (e.g., ArcGIS, address/parcel data), or strong willingness to learn quickly.
• Experience in B2B SaaS (or similarly complex product domains) where data correctness materially impacts user workflows.
• Experience partnering closely with product/engineering teams to turn recurring operational pain into playbooks, tooling improvements, and system-level fixes.

Tech Stack

• SQL (core for validation, reconciliation, troubleshooting)
• Python (data workflows and automation)
• Airflow (data migration pipelines)
• PostgreSQL (core database)
• RedwoodJS, GraphQL, TypeScript, AWS (broader environment)