{"id":1257577,"url":"https://alion.io/job/honeywell-sr-data-engineer","title":"Sr Data Engineer","company":{"id":15856,"name":"Honeywell","domain":"honeywell.com","url":"https://alion.io/company/honeywell-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":83,"open_postings":80,"ghost_share":0,"stale_share":0.9,"repost_share":0,"time_to_fill_p50_days":19,"computed_at":"2026-10-15T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":112000,"max_usd":214000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1181},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"RAG","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-04-07T22:00:51Z","employer_posted_date":"2026-04-07","last_verified_at":"2026-10-15T05:22:12Z","board_verified":true,"closed_at":null,"days_open":190,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":190},"description":"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.\nYou 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.\nKEY RESPONSIBILITIES\nData Engineering & AI Pipeline Development:\nDesign 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\nBuild and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows\nDevelop and optimize RAG (Retrieval Augmented Generation) systems, including vector databases, embedding pipelines, and efficient retrieval mechanisms\nLead the architecture and development of scalable data platforms on Databricks \nDrive the integration of GenAI capabilities into data workflows and applications \nOptimize data processing for performance, cost, and reliability at scale\nCreate robust data integration solutions that combine industrial IoT data streams with enterprise data sources for AI model training and inference\nDataOps:\nImplement DataOps practices to ensure continuous integration and delivery of data pipelines powering AI solutions\nDesign and maintain automated testing frameworks for data quality, data drift detection, and AI model performance monitoring\nCreate self-service data assets enabling data scientists and ML engineers to access and utilize data efficiently\nDesign and maintain automated documentation for data lineage and AI model provenance\nCollaboration & Innovation:\nPartner with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment\nMentor team members and provide technical leadership on complex data engineering challenges \nEstablish data engineering best practices, including modular code design and reusable frameworks \nDrive projects to completion while working in an agile environment with evolving requirements in the rapidly changing AI landscape\n YOU MUST HAVE\nMinimum 5 years of experience building production data pipelines in Databricks processing TB scale data\nExtensive experience implementing medallion architecture (Bronze/Silver/Gold) with Delta Lake, Delta Live Tables (DLT), and Lakeflow for batch and streaming pipelines from\nEvent Hub or Kafka sources \nStrong hands-on proficiency with PySpark for distributed data processing and transformation \nStrong experience working with cloud platforms such as Azure, GCP and Databricks, especially in designing and implementing AI/ML-driven data workflows\nProficient in CI/CD practices using Databricks Asset Bundles (DAB), Git workflows, GitHub Actions, and understanding of DataOps practices including data quality testing and observability\nHands-on experience building RAG applications with vector databases, LLM integration, and agentic frameworks like LangChain, LangGraph\nNatural analytical mindset with demonstrated ability to explore data, debug complex distributed systems, and optimize pipeline performance at scale\nWE VALUE\nExperience building RAG and agentic architecture solutions and working with LLM-powered applications \nExpertise in real-time data processing frameworks (Apache Spark Streaming, Structured Streaming) \nKnowledge of MLOps practices and experience building data pipelines for AI model deployment \nExperience with time-series databases and IoT data modeling patterns \nFamiliarity with containerization (Docker) and orchestration (Kubernetes) for AI workloads \nStrong background in data quality implementation for AI training data \nExperience working with distributed teams and cross-functional collaboration \nKnowledge of data security and governance practices for AI systems\nExperience working on analytics projects with Agile and Scrum Methodologies\nBENEFITS OF WORKING FOR HONEYWELL\nIn 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.\nABOUT HONEYWELL\nHoneywell International Inc. (Nasdaq: HON) invents and commercializes technologies that address some of the world's most critical challenges around energy, safety, security, air travel, productivity, and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency, productivity, sustainability, and safety in high-growth businesses in broad-based, attractive industrial end markets. Our products and solutions enable a safer, more comfortable, and more productive world, enhancing the quality of life of people around the globe.\nTHE BUSINESS UNIT\nHoneywell Connected Enterprise (HCE) is the software division of Honeywell with a strategic focus on digitization, sustainability, and OT Cybersecurity SaaS offerings and solutions. HCE was established to leverage Honeywell’s domain expertise and lead the transition into a cutting-edge industrial software company. Since our inception in 2018, HCE established the category of intelligent operations and built a new platform born out of decades of operational data and insights, uniting real-time data across assets, people, and processes into a system of record for a 360-degree view. This is our flagship offering - Honeywell Forge. We are a global team of thousands of innovators with expertise spanning industrial operations, software engineering, data science, artificial intelligence, and process engineering. We are paving the way for our customers to grow responsibly. We believe the future is what we make it. As a Honeywell Futureshaper, you are a part of something bigger. You can work with highly capable people to make the world a better place and become the best you. After all, we are not imagining the future; we’re building it.\nThe application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. 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