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Streaming Data Engineer

bright vision technologies·US·United States·mid
kafkadevopssre
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Streaming Data Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: Streaming Data Engineer Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $78,000–$105,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary We are seeking an experienced Streaming Data Engineer to architect, deploy, and operate large-scale Apache Kafka and Confluent platform environments supporting mission-critical event-driven workloads. In this role you will own the Kafka platform end-to-end, including cluster sizing, configuration, security, automation, observability, and developer enablement. The ideal candidate will combine deep Kafka internals knowledge with strong DevOps and SRE practices, and will partner with application teams to deliver a reliable, performant, and developer-friendly streaming platform. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Required Qualifications Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. Five or more years of experience operating Apache Kafka or Confluent Platform in production. Deep, hands-on knowledge

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