Software Engineer - Backend

About the role

LakeFusion is seeking a Software Engineer - Backend to build and scale our real-time API to enterprise production workloads. This is a deeply technical role focused on building resilient, fault-tolerant, high-throughput distributed systems that bridge enterprise backends with high-performance Python microservices and data lakehouse platforms.

A critical dimension of this role: we ship into customer environments. You won’t just write code that works on our infrastructure — you’ll build systems robust enough to run across Azure, AWS, and customer-managed Kubernetes clusters where you don’t control the network, the identity provider, the observability stack, or the operator. That requires a particular kind of engineering discipline: defensive design, strong observability, minimal environmental assumptions, and thorough testing under failure conditions.

What you’ll do

• Scale the real-time API: Own the architecture and implementation of our high-throughput, low-latency API built in Python/FastAPI and deployed on Kubernetes.
• Maintain enterprise core: Develop and maintain core enterprise backends using .NET / C#, ensuring seamless interoperability between .NET services, FastAPI microservices, and NoSQL datastores.
• Build for resilience and fault tolerance: Design for graceful degradation under upstream failures, rate limits, partial outages, and retry storms. Eliminate silent failure modes.
• Engineer for autoscaling: Tune Kubernetes autoscaling behavior (HPA, KEDA, cluster autoscaler) to handle bursty production workloads across diverse customer environments.
• Optimize NoSQL data stores: Architect and optimize MongoDB collections, aggregation pipelines, indexing strategies, and asynchronous database connections for low-latency queries.
• Build streaming integrations: Develop streaming pipelines and background execution workflows that sync data between service, transactional, and analytical layers.
• Integrate Lakehouse analytics: Connect backend APIs with Databricks compute, Delta Lake tables, and Unity Catalog to expose real-time analytics and data pipelines to downstream consumers.
• Design for portability: Architect services that ship into customer environments and work reliably without serverless dependencies, with customer-controlled identity, restricted network egress, and varied observability stacks.
• Own performance: Profile, benchmark, and optimize for latency and throughput. Understand and tune the full stack — from FastAPI event loops and .NET memory management through connection pooling, caching, and downstream service calls.

What we’re looking for

• 2+ years of backend engineering experience building production-grade services and microservices.
• 2+ years of deep Python expertise, with hands-on mastery of FastAPI, ASGI server tuning (uvloop/Uvicorn), async event loops (asyncio), Pydantic, and performance profiling.
• 2+ years of hands-on experience in .NET Core / .NET 6+ (C#), building RESTful APIs, Dependency Injection architectures, Entity Framework Core, and enterprise backend components.
• 2+ years of experience working with MongoDB, including schema design, aggregation pipelines, index tuning, and asynchronous drivers (Motor, PyMongo).
• Strong Kubernetes experience, particularly around autoscaling (HPA, KEDA), resource allocation, containerization (Docker), and production deployment patterns.
• Resilience and fault-tolerance engineering: circuit breakers, retries with backoff and jitter, bulkheads, timeouts, idempotency, and graceful degradation patterns.
• Deployment portability experience: you understand how to write software that runs in environments you don’t control and can adapt to restricted network egress and custom identity providers.
• Strong observability instincts: structured logging, metrics, tracing (OpenTelemetry), and the discipline to make systems debuggable from the outside.
• Excellent technical communication, with experience collaborating across distributed engineering teams.

Nice-to-have

• Databricks experience: Familiarity with Databricks Connect, Delta Lake, Spark SQL, or MLflow integrations.
• Experience with event-driven architectures and streaming platforms (Kafka, RabbitMQ, or AWS EventBridge).
• Experience deploying into enterprise customer environments with strict security/compliance constraints (customer-managed identity, private networking, private endpoints).
• Experience with dual-paradigm data access pattern…