Senior Backend Engineer

We already have a production system in place with live customers. Our platform processes millions of transactions daily while maintaining sub-100ms API response times, powered by PostgreSQL, ClickHouse, ElasticSearch, and Kafka-based ingestion pipelines. Now we’re expanding the platform’s capabilities, broadening the API surface, and building the next generation of developer tools on top of it., We are looking for an experienced Backend Engineer who loves working with data, building robust services, and optimising distributed systems. You will work across ingestion pipelines, API layers, and query engines, shaping how our platform stores, processes, and serves financial data.

This is a hands-on role where you will make architectural decisions, solve scaling challenges, and design backend systems that form the foundation for AI-powered financial workflows.

What you will do

• Design, implement, and optimise backend services that power our APIs, integrations, and developer tools.
• Own database performance and reliability: schema design, indexing, query optimisation, and scaling strategies for PostgreSQL and ClickHouse.
• Build and maintain high-throughput ingestion pipelines with Kafka, transforming raw accounting data into well-structured, queryable formats .
• Develop data access APIs that make financial data securely and efficiently available to both internal services and external developers.
• Improve observability across the stack: logging, metrics, tracing, and alerting for ingestion pipelines and backend services.
• Collaborate with engineers, product, and domain experts to turn customer workflows into reliable backend systems.
• Contribute to architectural decisions across event-driven workflows, sync/transform services, indexing strategies, and search infrastructure.
• Support AI-powered workflows by ensuring data pipelines are structured, reliable, and optimised for retrieval-augmented generation (RAG), schema validation, and downstream agent evaluation.
• Help design guardrails and evaluation hooks for systems where agents or customers write data back, ensuring correctness, performance, and safety.
• Explore new data architectures such as graph databases to model complex financial relationships and support emerging AI workflows.
• Work in lockstep with AI engineers to evolve Translucent into an AI-first company.

• 5+ years of backend engineering experience, ideally in data-intensive or high-scale systems.
• Strong skills in SQL and database design (PostgreSQL preferred), with proven experience in optimisation and scaling.
• Solid programming skills in Python, Go, Kotlin or a similar backend language.
• Experience designing and maintaining production APIs (REST/gRPC/tRPC).
• Comfort with cloud platforms (AWS, GCP, or similar) and containerised environments (Docker, Kubernetes).
• Understanding of data pipelines and ETL processes, working with both structured and semi-structured data.
• Exposure to AI-powered data frameworks and infra such as vector databases (Weaviate, Qdrant, Pinecone), graph databases (Neo4j, Memgraph), or model serving/routing systems (vLLM, LiteLLM).
• Familiarity with agent orchestration frameworks (LangGraph, Mastra) or AI observability tools (W&B Traces, Arize, OpenLLMetry).
• Experience building data pipelines optimised for AI workflows, e.g. embedding search, feature stores, or schema-validated outputs.
• Clear, pragmatic communication and a collaborative approach.
• An interest in joining a small, fast-moving startup where you can influence product direction and engineering culture.

Nice to have

• Experience with event-driven architectures and streaming systems (Kafka, Pub/Sub, etc.).
• Familiarity with ClickHouse, ElasticSearch, or vector databases for high-performance queries and indexing.
• Background in accounting, finance, or enterprise SaaS.

Exposure to AI/ML-powered applications, LLM frameworks, or retrieval-augmented generation (RAG).

Translucent is building the infrastructure that lets AI work reliably with complex accounting data. Most AI fails when it hits real financial data since it is complex, inconsistent, and spread across dozens of systems. We have solved this problem by creating a platform that ingests, rebuilds, and indexes data from providers like Xero and QuickBooks, enabling developers to create AI-powered workflows that are secure, accurate, and auditable.