Senior Backend Engineer (Python & TypeScript, Microservices & Data Pipelines)
EquiMatch GmbH · Berlin
What this role requires
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Also mentioned, not required: Node.js, Encore, Prefect, NSQ, Docker, GitHub Actions, Grafana, Tempo, ClickHouse, Pydantic, pytest, FastAPI, English (B2). Worth having, but their absence is not what gets a CV filtered out.
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Job description
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 Node.js microservices.
We’re looking for a Senior Backend Engineer who is strong in Python, solid in TypeScript, and used to owning distributed systems in production. You’ll split your time between Python services and pipelines and TypeScript services built on Encore, and you’ll be expected to set technical direction, not just execute on it. We value engineers who use AI daily to move faster and build better.
Tasks
Design, build, and operate Python and Node.js (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).
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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.
Requirements
6+ years of professional backend experience, with at least 4 years of Python in production.
2+ years of professional TypeScript/Node.js 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/Node.js) 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
Benefits
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
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