About the service
I build production streaming pipelines on Apache Flink that turn event streams into instant and reliable analytics. If data arrives in batches now and insights lag reality by hours, I close that gap. I design jobs around event time and watermarks, so late and out-of-order events are handled correctly, not silently breaking metrics. State backends, checkpoints, and savepoints I tune for high throughput and fast recovery from failure.
The top priority is correctness under load. I implement end-to-end exactly-once semantics: transactional sinks where supported, idempotent writes everywhere else, so failover doesn't double charges and doesn't lose orders. I build streaming joins across multiple sources, enrich events with reference data, and catch fraud chains, funnel drop-offs, and SLA violations the moment they happen through Flink CEP. You get clean code, tested deployment, and documentation for your team; I work with Kafka, Kinesis, Pulsar, JDBC, and object storage.
— Event time and window aggregation
— Exactly-once, checkpoints, and savepoints
— Joins, enrichment, and Flink CEP patterns
— Real-time analytics, alerting, and monitoring
The top priority is correctness under load. I implement end-to-end exactly-once semantics: transactional sinks where supported, idempotent writes everywhere else, so failover doesn't double charges and doesn't lose orders. I build streaming joins across multiple sources, enrich events with reference data, and catch fraud chains, funnel drop-offs, and SLA violations the moment they happen through Flink CEP. You get clean code, tested deployment, and documentation for your team; I work with Kafka, Kinesis, Pulsar, JDBC, and object storage.
— Event time and window aggregation
— Exactly-once, checkpoints, and savepoints
— Joins, enrichment, and Flink CEP patterns
— Real-time analytics, alerting, and monitoring
