Senior Full Stack Engineer (Core Product)
Forager.ai ·
- Seniority
- Senior
- Category
- Fullstack
- Experience
- 5+ years
Forager.ai ·
About Forager.ai
Forager.ai is a leading provider of person and organization data, powering enrichment, search, and discovery for B2B platforms worldwide. Our APIs and bulk data feeds deliver billions of data points daily — used by waterfall enrichment platforms, data resellers, and B2B intelligence products to power their own customer experiences.
The Role
You'll build and operate the systems that deliver Forager's data to platform customers at scale: customer-facing apps, large-scale data pipelines, and search infrastructure. These are the surfaces our customers evaluate us on during competitive bakeoffs and rely on every day in production. You'll own end-to-end implementation across the stack — design, build, deploy, monitor, and iterate.
This is a senior, high-ownership role. You'll have direct input into product direction and the freedom (and responsibility) to ship work that materially moves our coverage, accuracy, latency, and uptime metrics.
What You'll Build
Real-time enrichment APIs — person/org lookup, contact data, reverse search for waterfall platforms. Match rate, latency, and freshness directly drive customer value.
Bulk data feed delivery — maintain the Snowflake service delivering billions of data points daily to Data Feed customers powering people/company search.
Elasticsearch search infrastructure — indexing, query design, relevance tuning, and cluster scaling for person/company search and filtering APIs.
ETL pipelines — workers, task queues, and transformations moving data into APIs and feed exports while maintaining fill rates and accuracy.
Customer-facing web app and developer experience — React/TypeScript app, docs, onboarding flows, and self-serve surfaces.
Core Responsibilities
Product & Application Development
Build and maintain Forager's customer-facing web app (React, TypeScript, Django/Python).
Implement and maintain RESTful APIs for integrations, feeds, and platform customer workflows.
Develop scalable backend services — workers, task queues, data pipelines — that keep refresh cycles predictable and fill rates high.
Participate actively in product planning; help shape which features have the highest customer impact.
Search, Data Layer & ETL
Build and operate Elasticsearch indices for people/company search — schema, ingestion, relevance, scaling.
Design and operate ETL applications moving data into searchable stores, feeds, and warehouses (Snowflake, S3).
Optimize PostgreSQL — query performance, indexing, cache utilization.
Drive measurable improvements in latency, uptime, error rate, and scalability.
DevOps & Infrastructure
Own day-to-day AWS infrastructure (ECS, S3, etc.) alongside DevOps.
Operate CI/CD, observability (Grafana, CloudWatch, Sentry), and on-call response for the surfaces you build.
Share crawler infrastructure maintenance with the team.
Collaboration & Quality
Code review with high standards for readability, security, and performance.
Write unit, integration, and E2E tests — test reliability is a quality contributor, not overhead.
Document features, architecture, and API contracts; great developer docs are how our customers succeed.
What We're Looking For
Required Experience
5+ years building and operating production web applications and APIs.
Strong proficiency in Python / Django and React / TypeScript .
Hands-on experience operating Elasticsearch at scale — schema design, query tuning, cluster management.
Production experience with PostgreSQL , Redis , and async task systems ( Celery / RabbitMQ or equivalent).
Demonstrated track record building and operating ETL pipelines that move significant data volumes reliably.
Comfortable with AWS (ECS, S3, CloudWatch) and CI/CD pipelines (GitHub Actions or equivalent).
Experience operating services in production — observability, on-call, incident response.
Strong written communication; comfortable owning documentation as a deliverable.
AI & Agentic Workflows (Required)
This is non-negotiable. You must demonstrate strong, hands-on fluency with:
AI coding tools (Claude Code, Cursor, Copilot, or equivalent) used daily for implementation, refactoring, and code review.
Agentic workflows — designing, orchestrating, and debugging multi-step agent pipelines (e.g., research → plan → implement → verify loops, MCP server integration, tool-use design).
Judgment about where AI helps vs. hurts — knowing when to delegate to an agent, when to write the code yourself, and how to keep an agent on rails for production work.
We evaluate this in interviews with live exercises. Candidates without demonstrable agentic workflow experience will not be considered.
Nice to Have
Experience with Snowflake or other data warehouses.
Background in B2B data products — enrichment, contact data, company data, search/discovery.
Experience with web crawling, data sourcing, or large-scale ingestion systems.
Open-source contributions or public technical writing.