Senior Data Engineer

This role with one of the very well known brand in the field of control systems and security solutions. Minimum 5+ years experience and has strong knowledge working on IoT systems and simulation.Data Platform & Pipeline Development Design, build, and maintain scalable data pipelines using Databricks and Azure Architect solutions processing large-scale structured and unstructured data from IoT devices, airport sensors, and operational systems Integrate real-time and batch data streams ensuring high performance, reliability, and data quality Design optimized data models for analytics of structured, semi-structured, and unstructured data Make architectural decisions on storage strategies, processing frameworks, and integration patterns Technical Leadership & Best Practices Mentor junior data engineers; establish coding standards, testing practices, and operational excellence Champion data governance, security, and compliance standards; drive CI/CD automation Lead technical design reviews and influence architectural decisions Proactively monitor, troubleshoot, and optimize pipeline performance and reliability Collaboration & Delivery Collaborate with Data Science, ML Engineering, Product, and Business teams to translate requirements into robust solutions Partner with Software Engineers and DevOps teams for seamless integration and deployment Document architectural decisions, data models, and processes for knowledge sharing Communicate technical concepts effectively to both technical and non-technical stakeholders We’re Looking for Someone With: 5+ years in data engineering, with strong track record of delivering scalable solutions in cloud environments Expert-level experience with Databricks and Azure Data Services Proven experience building production-grade data pipelines processing large-scale datasets Experience with microservices architecture using Docker, Kubernetes Experience with real-time data processing (streaming architectures, event-driven systems) Deep understanding of data modeling techniques: dimensional modeling, data vault, star/snowflake schemas Expert knowledge of ETL/ELT best practices and modern data integration patterns

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