Data Engineer
Honeywell
full-time
Posted on:
Location Type: Hybrid
Location: Atlanta • United States
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About the role
- 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
- 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 systems 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
- Drive continuous improvement in data engineering practices and tooling
- Establish best practices for data pipeline development and maintenance in AI contexts
- Drive projects to completion while working in an agile environment with evolving requirements in the rapidly changing AI landscape
Requirements
- Minimum 3 years of experience in data engineering with a strong grasp of Change Data Capture (CDC), ELT/ETL workflows, streaming replication, and data quality frameworks
- Deep expertise in building scalable data pipelines using Databricks, including Unity Catalog and Delta Live Tables
- Strong hands-on proficiency with PySpark for distributed data processing and transformation
- Solid 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 GitHub Actions, Bitbucket, Bamboo, and Octopus Deploy to automate and manage data pipeline deployments.
- Experience building solutions on RAG and Agentic architectures 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
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.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data engineeringChange Data Capture (CDC)ELTETLdata quality frameworksdata pipelinesPySparkreal-time data processingMLOpsdata modeling
Soft Skills
cross-functional collaborationagile environmentcontinuous improvementproject completioncommunication