We are seeking a highly specialized engineer in Real-time Streaming to evolve Coca-Cola’s enterprise data estate from batch-reliant processing to a continuous, event-driven ecosystem. In this role, your primary mission is to design, standardize, and codify streaming and Kappa Architecture patterns, minimizing redundant batch layers. 

Rather than just building isolated data pipelines, you will architect the foundational frameworks, reference implementations, and low-code configurations that enable engineering teams across the organization to instantly deploy robust, real-time data streams. The target destination for our real-time data ecosystem is Microsoft Fabric (Spark Streaming and/or Fabric Real-Time Intelligence). 

The ideal candidate is a champion of log-centric data design, possesses deep expertise in enterprise Apache Kafka environments. 

Core Responsibilities 

• Build a reusable streaming architecture playbook for the enterprise, with a strong focus on Kappa-style patterns, event-driven processing, and near-real-time analytics on Fabric.  

• Architect, document, and templatize standardized replay/backfill mechanisms (offset reset + checkpoint/state management + sink rebuild/dedupe strategy). 

• Contextualize Processing Latency by creating distinct architectural blueprints and guardrails tailored to business SLAs: sub-second real-time and near-real-time  

• Abstract Streaming Complexities by packaging advanced stream concepts (stateful processing, watermarking, late-arriving data remediation, and complex windowing) into reusable templates. 

• Codify strict design patterns for effectively-once / end-to-end idempotency with deterministic replay. 

• Build metadata-driven frameworks that gracefully handle upstream schema drift without interrupting or crashing 24/7 downstream streaming queries. 

• Act as the internal authority on streaming design patterns across Kafka/Event Hubs, designing scalable ingestion into Fabric. 

• Build standardized dashboards to track critical stream health metrics, including end-to-end data lag, processing throughput, backpressure, and poison-pill routing via Dead Letter Queues (DLQ). 

Required Qualifications & Experience 

• 8+ years of experience in data platform architecture, with a dedicated minimum of 3+ years architecting and operating production-grade Streaming and Kappa Architectures. 

• 4+ years of experience running Spark Streaming and Apache Kafka pipelines. Must understand topic partitioning strategies, consumer group mechanics, schema registries (Avro/JSON), and performance tuning for high-throughput streams. 

• Deep familiarity with stream processing engines such as Spark Structured Streaming, Kafka Streams, or Apache Flink. 

• Strong Scala and/or PySpark experience with stateful Structured Streaming. 

• Advanced knowledge of the internal mechanics of Delta Lake storage, specifically regarding append-only streaming sinks and checkpointing behaviors. 

• The "Framework" Mindset: A strong portfolio demonstrating the creation of developer-facing frameworks, custom connectors, or configuration-driven streaming engines that abstract infrastructure. 

• Ability to articulate complex streaming topologies and real-time concepts to both application developers and executive stakeholders. 

Preferred Qualifications 

• Confluent Certified Developer or Kafka Administrator certification. 

• Proven experience modernizing Lambda architectures toward Kappa-first designs. 

• Hands-on experience utilizing Fabric Real-Time Intelligence, including Fabric Eventstreams and KQL Databases. (Equivalent deep experience with Azure Event Hubs, Stream Analytics, or Databricks Delta Live).