Role Description 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 - 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. You will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions. You will be expected to raise the bar through: - Code review - Design review - 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. 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 of Kafka internals (partitions, replication, ISRs, consumer groups). - Strong experience with Kafka security (SASL, mTLS, ACLs, RBAC). - Hands-on experience with Kafka Connect, Schema Registry, and either Kafka Streams or ksqlDB. - Experience with HA/DR strategies for Kafka. - Strong scripting skills in Python, Bash, or Go. - Hands-on experience with infrastructure-as-code (Terraform, Ansible). - Working knowledge of observability tooling for Kafka. - Excellent troubleshooting, communication, and documentation skills. Preferred Qualifications - Confluent Certified Administrator or Developer credentials. - Experience operating Kafka on Kubernetes (Strimzi, Confluent Operator). - Exposure to managed Kafka services (AWS MSK, Azure Event Hubs Kafka API). - Familiarity with stream processing frameworks (Flink, Spark Streaming). - Experience with data governance and lineage for streaming data. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including: - Recruitment - Hiring - Training - Compensation - Promotion - Transfer - Leaves of absence - Termination - Layoffs - Recall BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements. • Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric. • Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads. • Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations. • Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout. • Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices. • Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance. • Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.

• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements. • Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric. • Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads. • Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations. • Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout. • Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices. • Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance. • Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.

• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements. • Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric. • Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads. • Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations. • Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture. • Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout. • Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices. • Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance. • Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.