Software Guidance & Assistance, Inc. (SGA, Inc.)
Full Stack Engineer (NY/NJ/DMV)
Tysons, District of Columbia, US
Role snapshot
Software Guidance & Assistance, Inc. (SGA, Inc.) is hiring a Full Stack Engineer (NY/NJ/DMV) on a full-time basis. This is a hybrid role based in Tysons, District of Columbia, US.
- Company
- Software Guidance & Assistance, Inc. (SGA, Inc.)
- Location
- Tysons, District of Columbia, US
- Work mode
- Hybrid
- Job type
- Full-time
- Posted
- Nov 2, 2026
Who this role suits
The listing does not state a required experience level here. Read the requirements in the description below and apply if they match your background.
How to prepare your application
- Match your résumé to the requirements in the description, using the same wording where it is honest to do so.
- Prepare a short example for each requirement you meet, and be ready to explain any you only partly meet.
- Read what Software Guidance & Assistance, Inc. (SGA, Inc.) publishes about its product or team, so you can say clearly why this role and this company.
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About the role
Software Guidance & Assistance, Inc., (SGA), is searching for a Full-Stack Engineer for a CONTRACT assignment with one of our premier Regulatory clients. Must be local to one of these office locations and willing to work hybrid in DMV area or NY/NJ area. Full-Stack Engineer with deep systems-integration expertise to help define and build the workflow and triage capabilities for decision analytics system. Expected to create new capabilities from scratch while tightly integrating with existing capabilities/platforms (data, query, catalog, case-management, and legacy tools) during a multi-year, incremental (strangler-fig) migration. It suits an engineer who is equally comfortable owning a feature end-to-end (UI → API → data) and reasoning about contracts between systems. Responsibilities :
- Build greenfield workflow capabilities — triage, intelligent assignment, disposition, and handoff flows — from UX through backend, with production-grade rigor (testing, observability, security, accessibility).
- Integrate across the ecosystem — define and implement interfaces to data/query layers, metadata catalogs, case-management, and legacy surveillance systems; treat each cross-system handoff as a contract, not a wiring job.
- Do integration discovery — reverse-engineer how existing systems work, map data/interface dependencies, prototype integration approaches, and turn ambiguity into documented, buildable interface contracts.
- Support incremental migration — build for coexistence of old and new systems (dual-run, adapters, intake routing) rather than big-bang cutover.
- Leverage AI tooling — use AI-assisted development (code generation, spec-driven workflows, agentic accelerators) to raise throughput and quality; help establish patterns for the team.
- Collaborate across disciplines — work with product, data science, UX, and domain/business partners to turn requirements into working software.
- Strong full-stack engineering: modern front-end (e.g., Angular/React/TypeScript) and backend services (e.g., Java/Spring, Node, or Python) — comfortable owning a feature across the whole stack.
- Demonstrated systems-integration experience: designing and building APIs/interfaces between distinct systems; working with heterogeneous data sources, event/messaging patterns, and third-party/legacy integrations.
- Development rigor — testing discipline, code quality, observability, secure-by-default patterns, and an instinct for maintainable design under real production constraints.
- Comfort operating in ambiguity / discovery — able to investigate unfamiliar systems, ask the right questions, and produce clear technical designs and interface definitions.
- Fluency with AI-assisted development tools and a track record (or strong appetite) for using them to accelerate delivery without sacrificing quality.
- Cloud experience (AWS preferred) — building and integrating services in a cloud environment.
- Data-savvy: comfortable with SQL and large/structured data; able to work with data-science partners on analytics/ML-backed features (you don't need to be a data scientist, but you should speak the language).
- UX sensibility: strong sense for analyst-facing usability; able to collaborate closely with UX or prototype UI when needed.
- Experience with BI/visualization platforms (e.g., QuickSight) and embedding/composing visual components.
- Experience with regulated / high-stakes domains (financial services, compliance, surveillance) where auditability and correctness matter.
- Experience replacing or strangler-fig-migrating (or other pattern) a legacy platform incrementally.
- Experience with MCP / LLM-backed tooling or building agentic/automation accelerators.