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Report directly to the Data Engineering Manager, based in Atlanta, GA, working on a hybrid schedule. In the first 90 days, new hires must work 100% onsite Monday‑Friday.
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 Retrieval Augmented Generation (RAG) 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.
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 systems 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.
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.
Qualifications YOU MUST HAVE
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.
WE VALUE
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.
US PERSON REQUIREMENT Due to compliance with U.S. export control laws and regulations, the candidate must be a U.S. Person: a U.S. citizen, a U.S. permanent resident, or possess protected status under asylum or refugee status or have the ability to obtain an export authorization.
Benefits In addition to a competitive salary, Honeywell employees are eligible for a comprehensive benefits package including 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 (vacation, personal business, sick time, parental leave); and 12 Paid Holidays.
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Languages
- English
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