Grants Engine: Engineering Manager
Child Poverty Action Lab
Grants Engine: Engineering Manager
Child Poverty Action Lab
Data-driven efforts to reduce child poverty
Grants Engine: Engineering Manager
Child Poverty Action Lab / Full-time / Remote
About the Role
The Grants Engine is an AI-powered tool that helps public agencies and community organizations find, prioritize, and pursue federal and state funding. It combines a continuously updated grant clearinghouse built from automated scrapers, AI-driven synthesis of long funding notices into the eligibility criteria that actually matter, recommendations based on organizational profiles and priorities, and grant pipeline and cohort management tools. Our goal is to put public funding within reach of organizations that do not have grant-writing staff.
We are live with beta users, onboarding our first statewide partner, and moving toward general access, with rapid state-by-state expansion of grant coverage.
Today the product is built by two contracted development firms (application and data ingestion), an infrastructure engineer, and part-time support from CPAL's centralized engineering team. Automated testing and regression testing, standardized deployment, logging, and monitoring are now in place across the application and scraper pipeline.
What we do not have is a dedicated owner for this layer. With work split across vendors, no single person holds the architecture and technical quality of the whole system, and downstream impacts of one team's decisions surface later than they should.
As our first engineering manager, you will bring technical ownership in-house: assess what is actually in place, decide what to keep, what to deepen, and what to replace, and then own it from there.
Responsibilities
Own technical implementation of the product roadmap end to end, from scoping through delivery.
Partner with the Product Lead to scope features, size effort, and plan resourcing. Surface downstream impacts and tradeoffs early, in terms non-engineers can weigh.
Translate partner commitments (data integrations, custom configuration, API work) into realistic technical plans and timelines.
Own the architectural direction of both the application and the data ingestion pipeline, and make sure the two are designed as one coherent system rather than two products that happen to share a database. Set and enforce the standard for what "done" means (test coverage, observability, documentation) through design discussions and code review.
Extend our testing practice into the evaluation infrastructure our AI workflows require. A wrong summary can cost an organization weeks on an ineligible application.
Own our engineering practices (testing, deployment, logging, monitoring): deepen coverage, close the gaps, and make sure they are consistently followed rather than nominally in place.
Build operational discipline: a real cadence for reviewing logs and alerts, clear ownership when something breaks, and the habit of catching problems before users report them.
Serve as the single point of technical accountability across our contracted and internal engineers. Work with the Product Lead to set shared standards and communication cadence, and hold contracted partners to them.
Help define, recruit for, and hire the engineering roles we add as we scale, including deciding where contracted work should move in-house. Onboard and mentor engineers, and raise the technical bar of everyone around you.
Engineering <> Product
The Engineering Lead (EL) reports to the Product Lead (PL). The PL owns roadmap strategy, prioritization, and phasing, and is accountable for what we build and why. The EL owns how we build it and is accountable for technical quality, architecture, and delivery.
This is a reporting line, but the working relationship is a partnership. The EL is expected to push back on sequencing, scope, and timelines when the technical reality warrants it, bringing an alternative rather than only an objection, and to raise infrastructure and technical debt early enough to shape the roadmap.
Architecture, technical design, engineering standards, and technical hiring decisions are the EL's call.
Prioritization tradeoffs, partner commitments, and scope decisions are the PL's call, made with the EL's input.
Where the two conflict, the expectation is that it gets worked out between them with the tradeoffs made explicit, not escalated by default.
A working sign of success in this partnership is that the PL is no longer coordinating between development teams, chasing deployments, or personally verifying that downstream technical impacts were considered.
What we are looking for
Significant hands-on software engineering experience on production systems, with a track record of leading engineers as a team lead, engineering manager, or technical lead.
Hands-on: you will read code, review pull requests, debug production issues, and write code when it is the fastest path.
Pragmatic: you know which corners are safe to cut at this stage and which are not, and you can explain the difference to a non-engineer.
Sound judgment about existing systems. You have taken a team from "we have this" to "this actually works and everyone follows it" (test coverage, CI/CD, observability, incident response, code review) without grinding delivery to a halt, strengthening what is worth keeping rather than rebuilding by default.
Strong systems and architecture judgment, and the ability to quickly understand a codebase you did not write, with enough range across frontend, backend, data pipelines, and AWS infrastructure to make sound decisions, evaluate others' work, and lead an infrastructure migration with specialists.
Clear communicator who builds trust across teams, including with contractors you do not directly employ.
Ownership-driven and comfortable with ambiguity: you name problems and take them on rather than waiting for direction.
Helpful, not required
Experience with web scraping, ETL, or data pipeline systems at scale, particularly where upstream sources change without warning.
Experience building evaluation and quality infrastructure for LLM or AI-driven product features.
Experience managing or transitioning work from external development agencies to an internal team.
Experience in civic technology, government technology, or the nonprofit sector.
Logistics
Location: Remote, with quarterly travel to offices in Dallas
Compensation: $200K - $250K base
Data-driven efforts to reduce child poverty
CPAL analyzes and applies data to reduce child poverty in Dallas by serving as an unofficial research and development arm for the city. It focuses on benefits delivery, maternal health, housing, safety, and criminal justice, working with over 100 partner organizations to turn data insights into public programs, neighborhood initiatives, and policy changes. Its products come from data analytics and collaborative projects with public agencies, community groups, and policymakers, funded by grants, donations, and partnerships with public and private entities. Unlike groups that only publish reports, CPAL translates data into actionable strategies for improving services and economic mobility for children. The organization's goal is to break cycles of intergenerational poverty and expand economic mobility for Dallas-area families.
Explore more opportunities at Child Poverty Action Lab