Sr Data Engineer

Key Responsibilities

• Data Engineering & AI Pipeline Development
• Design and implement scalable data architectures to process high-volume IoT sensor data and telemetry streams, ensuring reliable data capture and processing for AI/ML workloads
• 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, including 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 that combine industrial IoT data streams with enterprise data sources for AI model training and inference
• Implement DataOps practices to ensure continuous integration and delivery of data pipelines powering AI solutions
• Create automated testing frameworks for data quality, data drift detection, and AI model performance monitoring
• Create self-service data assets enabling data scientists and ML engineers to access and utilize data efficiently
• Design and maintain automated documentation for data lineage and AI model provenance
• Collaborate 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
• Establish data engineering best practices, including modular code design and reusable frameworks
• Drive projects to completion while working in an agile environment with evolving requirements in the rapidly changing AI landscape

Qualifications

• 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
• Natural analytical mindset with demonstrated ability to explore data, debug complex distributed systems, and optimize pipeline performance at scale
• Experience building RAG applications with vector databases, LLM integration, and agentic frameworks like LangChain, LangGraph
• 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
• Experience with 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

About Honeywell

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.