Sr Data Engineer

Job-:
Engineering

Location:

Atlanta, GA, United States Workplace:
Hybrid

Schedule:

Full time Posted:
As a Senior Data Engineer, you will be part of a high-performing global team delivering advanced AI and data solutions for Honeywell’s industrial customers, with a focus on IoT and real-time data processing. In this role, you will design and implement scalable data architectures and pipelines that enable next-generation AI capabilities, including large-scale machine learning models, intelligent automation, and real-time analytics.

You will work closely with cross-functional teams to transform high-volume IoT telemetry into reliable, actionable insights that support Honeywell’s connected industrial solutions.
You will report directly to our Data Engineering Manager and you’ll work out of our Atlanta, GA location on a Hybrid work schedule.

Note:

for the first 90 days, new hires must be prepared to work 100% onsite M-F.

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 Data Ops 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

Qualifications YOU MUST HAVE 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, Git Hub Actions, and understanding of Data Ops practices including data quality testing and observability

Hands-on experience building RAG applications with vector databases, LLM integration, and agentic frameworks like Lang Chain, Lang Graph Natural  analytical mindset with demonstrated ability to explore data, debug complex distributed systems, and optimize pipeline performance at scaleWE VALUE 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…