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

Were 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