Lead Data Engineer

Role Overview

• Architect end-to-end data pipelines processing terabytes of IoT telemetry on Azure Databricks (PySpark DLT, Lakeflow) using medallion Lakehouse architecture.
• Design and optimize real-time ingestion pipelines from Azure Event Hub and Apache Kafka for high-volume industrial IoT telemetry.
• Build fault-tolerant, idempotent streaming architectures handling schema evolution, backpressure, and latency SLAs.
• Lead architecture reviews, set engineering standards, and drive decisions on data modeling, pipeline design, and platform evolution.
• Define technical direction for AI-ready data products including vector stores, embedding pipelines, and RAG-ready structured/unstructured data.
• Adopt emerging LLM orchestration frameworks (LangChain, LangGraph) to accelerate GenAI platform capabilities.
• Build production GenAI pipelines
• RAG workflows, document ingestion, PII anonymization and vector database infrastructure.
• Collaborate with data scientists and AI engineers to deliver high-quality, AI-ready datasets that improve downstream model performance.
• Enforce data governance, access control, and security policies; lead PII detection and anonymization strategies across the data platform.
• Champion CI/CD practices using GitHub Actions, DAB, Octopus, and Bamboo for automated, reliable pipeline delivery.
• Ensure compliance with enterprise security standards within the SDLC.
• Mentor engineers across seniority levels through code reviews, pairing, and technical coaching.
• Translate business and AI product requirements into clear technical roadmaps and execution plans.
• Partner with data scientists, product owners, and architects to align data investments with Honeywell's autonomy strategy.

Requirements

• 8+ years of data engineering experience with at least 2 years in a lead or senior role, demonstrating progression in technical complexity and team leadership.
• Hands-on experience building and operating medallion lakehouse architectures (Bronze / Silver / Gold).
• Deep expertise in Apache Spark / PySpark with production experience on Azure Databricks at scale.
• Strong proficiency with streaming platforms
• Apache Kafka and/or Azure Event Hub for real-time IoT data.
• Cloud data architecture skills (Azure preferred; AWS/GCP a plus) with experience designing scalable, cost-effective data lakes and warehouses using cloud-native services.
• Data modeling and schema design expertise for both transactional and analytical workloads, including dimensional modeling and data vault methodologies.
• Proven experience building data pipelines for GenAI or ML applications: RAG systems, embedding pipelines, and document ingestion.
• MLOps familiarity including model versioning, feature stores, and monitoring/observability for data and ML systems.
• Demonstrated ability to lead technical design reviews, mentor engineers, and drive architectural decisions with stakeholder buy-in.
• Proficiency in CI/CD using GitHub Actions for automating data pipeline deployments.
• Experience with LangChain, LangGraph, or other agentic AI orchestration frameworks.
• 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

US PERSON REQUIREMENTS: Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status In the U.S. under asylum or refugee status or have the ability to obtain an export authorization.

Tech Stack

• Apache
• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• IoT
• Kafka
• Kubernetes
• PySpark
• SDLC
• Spark
• Vault

Benefits

• In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package.
• This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays.