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Add Kafka developers who can reason about events, partitions, consumer behavior and failure recovery—not just connect a producer to a topic. DreamzTech matches your event contracts, throughput and latency targets, delivery semantics, platform, integrations and operating model to screened engineers, with practical evidence and client interviews before onboarding.
Hire an Apache Kafka developer to move from ad-hoc producers and consumers to controlled, production event streaming, with tested delivery semantics, documented performance and clear ownership of partitions, schemas and cluster operations. Need broader stream-processing engine talent too? See our hire Apache Spark developers and hire PySpark developers pages, or our platform-neutral hire data engineers team. For architecture and delivery beyond staffing, explore our modern data engineering services, data integration services, controlled data migration services and data security services.
Build event publishers and consumers with explicit keys, partitioning, serialization, acknowledgements, retry, idempotency, offset, error-handling and backpressure decisions appropriate to the business contract.
Configure, extend or operate source and sink connectors for approved databases, SaaS platforms, object storage and analytics systems. Define schema evolution, dead-letter handling, replay, secrets, connector tasks and operational ownership.
Implement stateless or stateful processing, joins, windows, aggregations and event-time logic with defined stores, changelog topics, serialization, testing, recovery and deployment behavior.
Design or implement approved topic conventions, keys, partitions, replication, retention and quotas on self-managed Kafka, Amazon MSK, Confluent Cloud or compatible services when approved. Validate capacity, failure domains, KRaft operations, version compatibility and portability constraints against the selected deployment model.
Inspect throughput, end-to-end latency, producer/consumer rates, lag, rebalances, request latency, under-replicated partitions, disk/network pressure and JVM behavior. Plan phased migrations, upgrades and cutovers with monitoring, runbooks, incident ownership and knowledge transfer before declaring production readiness.
Apply approved TLS, SASL, ACL, secret, network and audit controls. Coordinate event ownership, schema compatibility, retention and sensitive-data rules with the client’s security and governance teams.
Our Apache Kafka developers bring deep technical expertise across event-contract design, distributed-systems reasoning, delivery semantics and production operations.
When at-most-once, at-least-once or exactly-once behavior applies, and what the downstream system must do about it—reasoned through, not assumed.
Partition-key choices, ordering boundaries, consumer-group rebalancing and lag behind every producer and consumer decision.
Choosing a managed connector, a Streams topology or custom application code—measured against the integration and processing need, not a single default.
Avro, Protobuf or JSON Schema compatibility rules enforced through the Schema Registry so producers and consumers evolve without breaking each other.
Broker sizing, replication, quotas and KRaft-based cluster operations verified independently of any one managed platform’s packaging.
Contract and replay tests, consumer-lag alerting and rehearsed recovery drills so failures are caught, not discovered live.
Review a representative role profile, then request two or three current CVs matched to your event sources/consumers, throughput, latency, delivery semantics, platform, security requirements and support expectations.
DreamzTech will replace a blueprint with a verified client case only when the Kafka contribution, technology, result and permission are documented. Until then, every card below is a solution blueprint, not a completed client engagement.
Environment: E-commerce and logistics event platform
Core Technology: Apache Kafka, Java/Spring Boot producers & consumers, Avro, Schema Registry
Solution blueprint, not a client case: Event publishers and consumers are built with explicit keys, partitioning, serialization, retries and idempotency, replacing ad-hoc point-to-point calls. Accepted on duplicate/replay behavior, measurable latency and a documented runbook—not invented throughput gains.
Environment: Data integration and CDC
Core Technology: Kafka Connect, Debezium, Schema Registry, MirrorMaker 2
Solution blueprint, not a client case: Source and sink connectors move database and SaaS changes into Kafka with defined schema evolution, dead-letter handling and replay. Accepted on source-to-target reconciliation, restart behavior and ownership checks—not an unqualified real-time promise.
Environment: Real-time risk and operations monitoring
Core Technology: Kafka Streams, ksqlDB, state stores, changelog topics
Solution blueprint, not a client case: Stateful joins, windows and aggregations flag anomalies from live event streams, with defined state stores, changelog topics and recovery behavior. Accepted on correctness under late, duplicate and out-of-order events plus deployment evidence—not an unmeasured real-time claim.
Flexible Engagement Models | Fully Signed NDA | Code Security | Easy Exit Policy
A useful matching call begins with what’s streaming, where events move, which workloads are sensitive and what happens when a producer, consumer or broker fails. Share current event flows, sample workloads, execution evidence and access constraints.
Share your event workloads and platform and we will design the fastest path to a supportable, production-ready Kafka implementation.
Our Apache Kafka developers bring deep technical expertise across event-contract design, distributed-systems reasoning, delivery semantics and production operations.
| Kafka core | Apache KafkaBrokersTopicsPartitionsKRaftProducersConsumersConsumer GroupsAdmin APITransactions |
| Application development | JavaScalaPythonGoC#/.NETSpring BootREST/gRPCApproved client libraries |
| Integration and CDC | Kafka ConnectSource/Sink ConnectorsDebeziumSchema RegistryREST ProxyMirrorMaker 2 |
| Stream processing | Kafka StreamsksqlDBApache FlinkSpark Structured Streaming where justified |
| Schemas and formats | AvroProtobufJSON SchemaJSONGoverned compatibility/versioning policies |
| Managed platforms | Amazon MSKConfluent CloudAzure Event HubsApproved vendor services |
| Deployment and IaC | LinuxDockerKubernetesHelmTerraformGitCI/CD pipelines |
| Observability | JMX metricsPrometheusGrafanaOpenTelemetryLogsTracesConsumer lagAlertsRunbooks |
| Security | TLS/SSLSASLACLsSecrets managementNetwork controlsAudit integrationData governance |
| Data ecosystem | DatabasesObject storageWarehouses/lakehousesElasticsearch/OpenSearchAPIsApproved enterprise applications |
Hire dedicated Apache Kafka developers for your project with our quick, efficient, and hassle-free hiring process. Build your event-streaming team faster and accelerate innovation by onboarding top Kafka professionals.
Tell us your Kafka workloads, systems and backlog. We will quickly match the right Kafka talent to your project.
We connect you with pre-vetted Kafka developers ready to deliver. Review profiles, interview, and select the best fit for your event platform.
Confirm a realistic start date once availability, interviews, contracting, cluster/cloud access, security review and process-owner availability are known.
Hire Apache Kafka developer(s) who deliver reliable, auditable event-streaming pipelines across various industries to help businesses operate with confidence.
Strong Kafka delivery combines distributed-systems judgment with event-contract discipline, security and operational ownership. DreamzTech can connect the Kafka developer to cloud, data engineering, BI, QA, security and product specialists when the backlog crosses role boundaries. For platform-neutral pipeline consulting beyond dedicated staffing, see our data engineering services.
Share your event workloads, systems, cloud platform, delivery semantics, performance targets and staffing gap. We will respond with the likely developer profile, readiness questions and a practical first scope.
Got questions about hiring an Apache Kafka developer? Explore the FAQs below.
Apache Kafka is an open-source distributed event-streaming platform. Applications publish events to topics, Kafka stores those streams durably, and consumers read or process them. Teams use Kafka to connect systems, support event-driven applications and move or process continuous data across distributed environments.
A Kafka developer builds and operates producer, consumer, Kafka Connect or Kafka Streams workloads. Typical responsibilities include event and schema design, partition-key choices, serialization, retries, offsets, delivery semantics, integration, testing, monitoring, security controls, performance diagnosis, deployment and production handoff.
Match skills to ownership. Application roles need producer/consumer APIs, schemas, partitioning, retries, idempotency, offsets and testing. Streams roles need event time, joins, windows and state. Connect roles need connectors, task scaling and recovery. Platform roles need KRaft, replication, capacity, upgrades, security, monitoring and incident response.
Kafka is used for event-driven applications, continuous data integration, CDC, stream processing, activity tracking, logistics or IoT event ingestion, real-time analytics inputs and communication between decoupled services. The right fit depends on throughput, retention, ordering, replay and operational requirements—not simply whether data is “real time.”
Kafka is built around durable, partitioned event logs that consumers can replay, while RabbitMQ is commonly used as a message broker for queues and routing. Either can fit reliable messaging. Decide using retention and replay needs, ordering scope, routing, consumer behavior, throughput, latency, operating model and team experience rather than treating one as universally better.
Kafka Streams is a client library for building applications that transform or analyze events, including joins, windows, aggregations and stateful processing. Kafka Connect is a framework for moving data between Kafka and external systems through reusable source and sink connectors. Some systems use both.
Start with a representative workload and defined correctness and durability requirements. Measure end-to-end latency, throughput, producer/consumer rates, lag, rebalances, broker request latency, partition balance, disk/network pressure and JVM behavior. Change a justified bottleneck, then rerun the same workload and record gains and tradeoffs.
Cost depends on seniority, whether the role covers applications, Kafka Connect, Kafka Streams or cluster operations, the managed platform, security requirements, engagement duration, timezone overlap, urgency and whether you need one developer or a pod. DreamzTech should provide matched profiles and a written rate after reviewing the event workload.