Data Engineer
Binti is hiring a Data Engineer to own the data layer behind its child-welfare reporting platform: building SQL transformation pipelines, dimensional models, and BI datasets so government agencies can run compliance and analytics reports. Hybrid, in-office in San Francisco with a Seattle satellite option.
- Compensation
- $200K–$300K / yr
- Location
- Binti HQ - San Francisco, CA
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5 to 10 years of software engineering experience, with a primary specialty in data and secondary specialty in backend web software
- Strong SQL skills; can reason about a large online application schema; seasoned in dimensional modeling with defensible opinions on grain and naming
- Experience building and maintaining transformation pipelines with tests, documentation, and lineage (not one-off scripts)
- Skilled in requirements translation: turning stakeholder descriptions (e.g., government compliance reports) into supporting data models
- Experience with a modern BI tool and its modeling layer (e.g., Looker, Sigma, Power BI)
- Documents and builds reusable assets and self-service tools that empower others
- Comfortable with ambiguity and crossing into adjacent areas of the stack as the first dedicated person in this function
- Eager, curious, and proactive in using modern AI tooling to accelerate engineering delivery end to end
- Passionate about using technology to improve outcomes for children and families in the child welfare system
Nice to have
- Government or gov-tech data experience
- Familiarity with child welfare data standards and federal reporting (AFCARS, NYTD, CCWIS)
Full description
Binti builds software for state and county government agencies, focusing on reinventing social services. We started in child welfare, with the mission of helping every child have a safe, loving, and stable family. To date, we’ve helped approve more than 100,000 families to foster or adopt, and we support over 49% of the nation’s child welfare system. We have expanded our product offerings in child welfare, moving more to the root of the problem, helping families stay together and avoid separation, and are now expanding horizontally across other areas in social services.
Binti is a for-profit, mission-driven software company based in San Francisco, CA. Investors include Founders Fund, First Round Capital, Kapor Capital, and others. We’re a team of ~90 people and growing quickly. We care about creating a workplace where everyone feels welcome and can bring their full self to work. We have a huge, ambitious vision to rewire government to be more effective in expanding opportunities for people around the world, and we are looking for mission-driven, high-empathy, high-performance, and low-ego team members to join us on our exciting journey towards that vision.
Binti builds software that child welfare agencies use to recruit, license, and support foster and adoptive families, and to keep track of the children in their care. Binti’s platform reporting capabilities enable agency administrators to improve outcomes for kids and families, by measuring the performance of their organization, demonstrating compliance, and making better allocation decisions.
We are looking for a Data Engineer to own the data layer that powers agency reporting. Our BI layer provides datasets in customer-friendly schemas for querying through Binti’s data platform tool. The Data Engineer owns the transformation of the data to best meet customers’ enterprise reporting needs.
LOCATION
Binti is headquartered in San Francisco, California. Much of our team is local to the San Francisco Bay Area and works via a hybrid setup with Monday & Thursday in office. Our Satellite office in Seattle, Washington, sets their own in-office days and hybrid policies, but in general employees are encouraged to join the leadership team on the days when they will be in the office for collaboration and team cohesion.
WHAT YOU WILL DODesign and build the transformation layer
- Build the transformations that turn internal models into clean analytical tables
- Make deliberate modeling choices: conformed dimensions, sensible grain, stable and human-readable naming, documented semantics, consistent handling of dates, statuses, and soft deletes
- Build for correctness under our security model, preserving authorization scoping and permissions logic
- Establish monitoring and alerting for data freshness and quality