2 days ago
Responsibilities
- Architect end-to-end terabyte-scale IoT telemetry pipelines on Azure Databricks using PySpark, Delta Live Tables, Lakeflow, and medallion lakehouse architecture.
- Design and optimize real-time ingestion using Azure Event Hub and Apache Kafka, including fault tolerance, idempotency, schema evolution, backpressure, and latency management.
- Lead architecture reviews, define engineering standards, drive data modeling and platform decisions, and shape technical roadmaps.
- Build AI-ready data products including vector stores, embedding pipelines, RAG workflows, document ingestion, and structured and unstructured data foundations.
- Collaborate with data scientists and AI engineers to deliver datasets and pipelines for GenAI and ML applications.
- Implement data governance, access controls, security policies, PII detection, and anonymization strategies.
- Automate data pipeline delivery through CI/CD tooling including GitHub Actions, DAB, Octopus, and Bamboo.
- Mentor engineers through code reviews, pairing, and technical coaching while partnering with product owners and architects.
Requirements
- At least 8 years of data engineering experience, including at least 2 years in a lead or senior role.
- Hands-on experience building and operating Bronze, Silver, and Gold medallion lakehouse architectures.
- Deep Apache Spark and PySpark expertise with production-scale Azure Databricks experience.
- Strong experience with Apache Kafka and/or Azure Event Hub for real-time IoT data.
- Experience designing scalable cloud data lakes and warehouses, preferably on Azure, with AWS or GCP experience as a plus.
- Expertise in data modeling and schema design for transactional and analytical workloads, including dimensional modeling and data vault methodologies.
- Experience building RAG systems, embedding pipelines, document ingestion, or other data pipelines for GenAI and ML applications.
- Familiarity with MLOps, model versioning, feature stores, monitoring, and observability for data and ML systems.
- Demonstrated ability to lead technical design reviews, mentor engineers, and drive architecture decisions with stakeholder support.
- Proficiency with GitHub Actions for automating data pipeline deployments.
- Experience with LangChain, LangGraph, Apache Spark Streaming, Structured Streaming, time-series databases, IoT data modeling, Docker, or Kubernetes is valued.
- Knowledge of AI data quality, security, governance, distributed collaboration, and Agile or Scrum methodologies is valued.
Benefits
- Hybrid work in Atlanta, Georgia; new hires must work fully onsite Monday through Friday for the first 90 days.
- Relocation support may be provided.
- Benefits include employer-subsidized medical, dental, vision, and life insurance; short- and long-term disability; 401(k) matching; flexible spending and health savings accounts; EAP; educational assistance; parental leave; paid time off; and 12 paid holidays.
- The application period is estimated at 40 days from the November 9, 2026 posting date, subject to change.
Tech Stack
Apache KafkaApache SparkAWSAzureBambooDatabricksDockerGitHub ActionsGoogle Cloud PlatformKubernetesOctopus Deploy
Categories
Data Engineering
About Honeywell
Honeywell is a public industrial technology company that sells aerospace systems, industrial automation, warehouse and building management technologies, and safety and security products to commercial, industrial, and government customers. Its portfolio spans avionics and propulsion, process controls and SCADA, warehouse automation, and IoT software such as Honeywell Forge. The company is headquartered in Charlotte, North Carolina and serves global markets across aviation, energy, logistics, and critical infrastructure.