Senior Kafka Data Engineer

Title : Senior Kafka Data Engineer Location: Remote Job Type : Contract Role Overview We are hiring a Senior/Staff Data Engineer to lead the architecture and development of enterprise‑scale streaming data pipelines, with Kafka as the core technology. This is a hands‑on builder and owner role involving architecture decisions, production support, and direct collaboration with onsite US clients. Key Responsibilities • Architect, build, and operate real‑time and near‑real‑time Kafka‑based pipelines • Lead development using Confluent Kafka (core focus) • Design and implement stream processing using: • Apache Flink (preferred) or • Apache Spark Structured Streaming • Define Kafka best practices for: • Topic design, partitioning, and delivery semantics • Error handling and retries • Backpressure management and performance tuning • Design and maintain batch and hybrid ETL pipelines using SQL Server • ETL tools preferred: SSIS • Own data quality, reliability, and observability across streaming pipelines • Work directly with onsite clients to: • Translate business requirements into technical solutions • Defend architectural decisions • Troubleshoot complex Kafka and streaming production issues • Mentor engineers and raise overall engineering standards • Collaborate with DevOps and platform teams in containerized, distributed environments Required Skills & Experience • 8+ years of hands‑on data engineering experience • Deep expertise in Confluent Kafka: • Architecture and cluster design • Topic and partition strategy • Delivery semantics (exactly‑once, at‑least‑once, etc.) • Strong hands‑on experience with: • Apache Flink (preferred) or • Apache Spark Structured Streaming • Excellent SQL skills (complex analytics, performance tuning) • Strong experience in Python, Java, or .NET (C#) • (At least one OOP language is mandatory) • Solid understanding of: • Streaming and event‑driven architectures • ETL / ELT patterns • Data modeling and orchestration • Experience with SQL Server Nice to Have • SSIS • Docker / Kubernetes • CI/CD for data pipelines • Cloud or hybrid data platforms