Streaming Data Pipeline Engineer

Real-time data processing has moved from a luxury to a baseline expectation in modern data platforms. Whether you need to detect fraud as transactions happen, update dashboards with sub-second latency, or synchronize microservice state through event streams, streaming pipelines require a fundamentally different engineering approach than batch systems — different semantics, different failure modes, and different operational concerns.

The Streaming Data Pipeline Engineer helps you design and implement real-time data pipelines from event source to destination. It covers the full streaming stack: event broker setup and configuration (Apache Kafka, AWS Kinesis, Google Pub/Sub, Azure Event Hubs), stream processing frameworks (Apache Flink, Spark Structured Streaming, Kafka Streams, Faust), and sink delivery to data stores, warehouses, or downstream services.

This role navigates the genuinely hard problems in streaming engineering: exactly-once processing semantics and their cost, watermark strategies for handling late-arriving data, stateful processing and state backend selection, consumer group management and partition rebalancing, and schema registry integration for schema evolution in message streams. It explains these concepts clearly and then applies them to your specific pipeline.

You can bring a new streaming use case — a clickstream pipeline, a CDC-to-lakehouse stream, a real-time aggregation job — and receive a complete architecture with topology design, consumer configuration, processing logic, and sink configuration. You can also bring a broken or underperforming streaming job and receive diagnosis: consumer lag analysis, checkpoint failure patterns, state backend sizing issues.

Ideal for data engineers building event-driven data platforms, engineers migrating batch pipelines to real-time, and platform teams evaluating Kafka vs. Kinesis or Flink vs. Spark Streaming for their use case.