Backend Engineer

About Company:

At Delaplex, we believe true organizational distinction comes from exceptional products and services. Founded in 2008 by a team of like-minded business enthusiasts, we have grown into a trusted name in technology consulting and supply chain solutions. Our reputation is built on trust, innovation, and the dedication of our people who go the extra mile for our clients. Guided by our core values, we don’t just deliver solutions, we create meaningful impact.

Job Description:
· 7-12+ years of backend/software engineering experience building enterprise-grade distributed systems, high-throughput services, and production microservices. · Strong hands-on Go experience: goroutines, channels, context cancellation, interfaces, error handling, memory/performance basics, profiling, testing, and clean package design. · Production microservices experience: REST/gRPC APIs, service boundaries, configuration, graceful shutdown, retries, circuit breakers, idempotency, and versioned contracts. · Strong event-driven architecture experience: queues, streams, pub/sub, outbox/inbox patterns, event replay, DLQs, eventual consistency, and exactly-once-vs-at-least-once tradeoffs. · Kafka experience beyond basic usage: partitions, keys, consumer groups, offsets, rebalancing, lag, retention, compaction, throughput tuning, and how horizontal scaling depends on partition design. · Hands-on messaging experience with Kafka, NATS JetStream, RabbitMQ, Pulsar, SQS/SNS, or similar messaging and streaming platforms. · Schema Registry and contract-governance basics: Avro, Protobuf, JSON Schema, schema compatibility, event versioning, and safe rollout of producer/consumer changes. · Strong PostgreSQL fundamentals: relational modeling, indexing, transactions, isolation basics, query tuning, migrations, connection pools, and operational troubleshooting. · Working knowledge of NoSQL systems such as MongoDB, Redis, DynamoDB, Cassandra, Elasticsearch/OpenSearch, and when to use them versus PostgreSQL. · Hands-on Docker and Kubernetes experience: images, deployments, services, probes, ConfigMaps/Secrets, resource sizing, logs, rollouts, and troubleshooting pods in production-like environments. · Good understanding of horizontal scalability: load distribution, batching, backpressure, rate limiting, queue-driven workers, worker pools, sharding, and concurrency limits. · Strong testing discipline: unit tests, integration tests, contract tests, testcontainers/local stack usage, load tests, race detection, and CI checks. · Security-aware engineering mindset: authentication/authorization basics, tenant isolation, secret handling, input validation, auditability, encryption, and least-privilege service access. · Clear communication and ownership mindset: ability to document design decisions, APIs, data contracts, operational runbooks, tradeoffs, and failure modes in a fast-moving startup environment. Nice to Have · Semantic technology exposure: hands-on or conceptual experience with RDF, OWL, SKOS, JSON-LD, Apache Jena, GraphDB, Neo4j, SHACL rules, ontology-backed validation, or graph projections in production systems. · Advanced distributed systems patterns: sagas, transactional outbox, CDC, event sourcing, materialized views, multi-tenant data isolation, replay-safe projections, and idempotent workflow orchestration. · Cloud-native platform experience: GCP/AWS/Azure services, managed Kubernetes, managed databases, object storage, service accounts/IAM, secret managers, and production deployment patterns. · Performance engineering: Go profiling with pprof, memory/CPU tuning, connection-pool tuning, database query analysis, queue throughput optimization, batching strategies, and high-volume event processing. · Observability stack: Prometheus, Grafana, OpenTelemetry, Loki/ELK/OpenSearch, distributed tracing, alert design, and operational dashboards. · DevOps collaboration: experience working with Azure DevOps, GitHub Actions, GitLab CI, container registries, Helm, Terraform, GitOps, and release automation. · Data formats and integration patterns: Protobuf, Avro, Parquet, JSON-LD, CSV/flat-file ingestion, EDI basics, webhook ingestion, canonical data model mapping, and schema evolution. · Platform engineering mindset: ability to design reusable internal libraries, service templates, shared observability modules, migration tooling, and developer-friendly operational patterns