Senior Backend Engineer (Python & TypeScript, Microservices & Data Pipelines)

EquiMatch is an AI-powered M&A origination platform connecting private equity firms search funds corporate development teams and M&A advisors with acquisition targets. Behind the product is a data-heavy platform: web crawlers LLM-driven company analyzers scoring pipelines a unified mailbox service and CRM integrations orchestrated across dozens of Python and microservices.
Were looking for a Senior Backend Engineer who is strong in Python solid in TypeScript and used to owning distributed systems in production. Youll split your time between Python services and pipelines and TypeScript services built on Encore and youll be expected to set technical direction not just execute on it. We value engineers who use AI daily to move faster and build better.

• Design build and operate Python and (TypeScript/Encore) microservices: define service boundaries APIs data flows and integrations.
• Build and maintain data pipelines crawling enrichment company analysis scoring orchestrated with Prefect.
• Model data and optimize queries on PostgreSQL (schema design migrations indexing query performance).
• Work with asynchronous messaging (NSQ) and background job queues to build resilient idempotent processing.
• Integrate LLM APIs into production workflows (extraction classification summarization) and make them reliable observable and cost-efficient.
• Own features end-to-end: requirements technical design implementation testing deployment on Railway and monitoring.
• Improve observability and performance across services and LLM calls: tracing metrics slow-query analysis failure-rate monitoring.
• Drive architecture decisions write technical designs and mentor other engineers through reviews and pairing.
• Contribute to automation (n8n) and internal tooling where needed.

• 6 years of professional backend experience with at least 4 years of Python in production.
• 2 years of professional TypeScript/ experience building backend services (Encore NestJS Fastify Express or similar).
• Deep experience with modern Python tooling: async/await type hints FastAPI or similar Pydantic pytest.
• Strong command of microservice architecture: service decomposition API design resiliency idempotency observability.
• Production experience with PostgreSQL schema design migrations query optimization.
• Experience with message queues or workflow orchestration (Prefect Celery NSQ RabbitMQ Kafka or similar).
• Track record of owning systems end-to-end in production including incidents and performance work.
• Habitual use of AI tools in day-to-day development; ability to apply AI effectively (prompting strategies code/test generation agentic workflows).
• Clear communication ownership mindset and a bias toward shipping.

Nice to have

• Encore experience specifically.
• Web scraping and crawling at scale: rate limiting proxies anti-bot handling content extraction.
• Building products on LLM APIs: structured outputs evaluation prompt versioning cost/latency tuning MCP servers.
• LLM observability and evaluation tooling (LangWatch Arize Phoenix or similar).
• Observability stacks (Grafana Tempo OpenTelemetry).
• Analytics tooling (PostHog ClickHouse).
• Docker and cloud deployment (Railway AWS GCP); Infrastructure as Code basics.
• Security best practices (OWASP OAuth2/JWT secrets management GDPR-aware data handling).
• Experience with email/CRM integrations (IMAP Gmail API Microsoft Graph HubSpot/Zoho).
• Familiarity with the M&A private equity or B2B data space.

Our stack

• Backend: Python microservices Encore (TypeScript/) services
• Orchestration & messaging: Prefect NSQ background job queues n8n
• Data: PostgreSQL ClickHouse (analytics)
• Infrastructure: Railway Docker GitHub Actions
• Observability: Grafana Tempo LangWatch Arize Phoenix (LLM tracing & evaluation)
• Analytics & tooling: PostHog Linear internal skills/MCP system
• AI: LLM-assisted development workflows LLM APIs in production MCP integrations

• Meaningful ownership of core systems in a small senior team
• Direct impact on a product used by PE firms and M&A professionals
• Support for AI-enhanced workflows and tooling
• Budget for learning conferences and hardware
• Flexible hours and remote-friendly culture