Sr Advanced Data Engineer

Honeywell•Atlanta, GA

41d

About The Position

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 DataOps: Implement DataOps practices to ensure 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 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 Collaboration & Innovation: 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 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

Requirements

Master's degree in computer science, Engineering, Applied Mathematics or related STEM field
5+ years building production data pipelines in Databricks processing TB scale data
3+ years of 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
2+ years of experience and hands-on proficiency with PySpark for distributed data processing and transformation
2+ years 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

Nice To Haves

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
Natural analytical mindset with demonstrated ability to explore data, debug complex distributed systems, and optimize pipeline performance at scale

Responsibilities

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
Design and maintain 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
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
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

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
For more Honeywell Benefits information visit: https://benefits.honeywell.com/

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