Senior Data Engineer

About This Role

As a Senior Data Engineer, you will design and implement scalable data architectures and pipelines focusing on IoT and real-time data processing for Honeywell’s industrial customers. This role involves transforming high-volume IoT telemetry into reliable, actionable insights to enable advanced AI and data solutions. You will work within a high-performing global team to deliver next-generation AI capabilities including large-scale machine learning, intelligent automation, and real-time analytics.

Responsibilities

• Design and implement scalable data architectures to process high-volume IoT sensor data and telemetry streams
• Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows
• Develop and optimize RAG (Retrieval Augmented Generation) systems, vector databases, embedding pipelines, and efficient retrieval mechanisms
• Lead the architecture and development of scalable data platforms on Databricks
• Drive the integration of GenAI capabilities into data workflows and applications
• Optimize data processing for performance, cost, and reliability at scale
• Create robust data integration solutions combining industrial IoT data with enterprise data sources for AI model training and inference
• Implement DataOps practices for continuous integration and delivery of data pipelines powering AI solutions
• Design and maintain automated testing frameworks for data quality, data drift detection, and AI model performance monitoring
• Partner with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment
• Mentor team members and provide technical leadership on complex data engineering challenges

Requirements

• Minimum 5 years of experience building production data pipelines in Databricks processing TB scale data
• Extensive experience implementing medallion architecture (Bronze/Silver/Gold) with Delta Lake, Delta Live Tables (DLT), and Lakeflow for batch and streaming pipelines from Event Hub or Kafka sources
• Strong hands-on proficiency with PySpark for distributed data processing and transformation
• Strong experience working with cloud platforms such as Azure, GCP and Databricks, especially in designing and implementing AI/ML-driven data workflows
• Proficient in CI/CD practices using Databricks Asset Bundles (DAB), Git workflows, GitHub Actions, and understanding of DataOps practices including data quality testing and observability
• Hands-on experience building RAG applications with vector databases, LLM integration, and agentic frameworks like LangChain, LangGraph
• Natural analytical mindset with demonstrated ability to explore data, debug complex distributed systems, and optimize pipeline performance at scale

Qualifications

• Minimum 5 years of experience building production data pipelines in Databricks processing TB scale data

Nice to Have

• Experience building RAG and agentic architecture solutions and working with LLM-powered applications
• Expertise in real-time data processing frameworks (Apache Spark Streaming, Structured Streaming)
• Knowledge of MLOps practices and experience building data pipelines for AI model deployment
• Experience with time-series databases and IoT data modeling patterns
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for AI workloads
• Strong background in data quality implementation for AI training data
• Experience working with distributed teams and cross-functional collaboration
• Knowledge of data security and governance practices for AI systems
• Experience working on analytics projects with Agile and Scrum Methodologies

Skills

* Required skills

Benefits