Data Engineer, Binti

• 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. You are seasoned in dimensional modeling. You have opinions about grain and naming and can defend them
• Experience building and maintaining transformation pipelines with tests, documentation, and lineage. Not one-off scripts
• Skilled in requirements translation. You can sit with a government program person describing a compliance report, understand what they actually need, and come back with a data model that supports it
• Experience with a modern BI tool and its modeling layer (eg. Looker, Sigma, Power BI)
• You document and build 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, you will define the practice and how it interfaces with related teams
• Eager, curious, and proactive in using modern AI tooling to accelerate engineering delivery end to end
• Passionate to use technology to improve outcomes for children and families in the child welfare system

Desirable

• 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
• 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
• Design reporting architecture in tandem with the core product lifecycle, ensuring the data model makes sense
• Support patterns including time series, cohorts, aging and time-in-status, compliance windows, and point-in-time snapshots
• Build the reusable pieces that can be composed to meet future needs
• Make sure the BI data model can express the module’s complex requirements
• Build stock and customer-specific reports, and enable the customer launch team to support customers
• Intake new agency reporting requirements and align them with solutions in line with our data strategy
• SAMPLE PROJECTS:
• Scoped embedded analytics. Validate that attribution in the data is reliable, build the scoped report, and pressure-test it so one organization can never see another's data
• Upsertable importers. Build idempotent, upsert-capable importers so we can ingest customer data on a recurring basis rather than as one-time loads
• Data mapping tooling. Improve the tools, documentation, and repeatable process for mapping a customer's legacy data model into ours. This is high leverage across every implementation we do
• Statewide reporting foundation. Build the analytics substrate that large statewide deployments require, including federal reporting obligations and agency-wide dashboards