Lead Data Engineer

Lead Data Engineer

We have an opportunity for a Lead Data Engineer to join us at Honeywell, in Dublin, where you will be part of a high-performing global team that delivers cutting-edge AI/ML data products for Honeywell's Industrial customers, with a specific focus on IoT and real-time data processing. As a data engineer, you will architect and implement scalable data pipelines that power next-generation AI solutions, including Large Language Models (LLMs), autonomous agents, and real-time inference systems. You will work at the intersection of IoT telemetry data and modern AI technologies to create innovative industrial solutions.

This is a hybrid role, 3 days at the office + 2 days work from home model, with the opportunity to travel to India and US if required.

Honeywell

We have been innovating for more than 100 years and now we're creating what's next. Our programs serve a global community and our hometowns too. We provide resources and financial support to causes around the world and encourage employee volunteerism. We are committed to the highest standard in everything we do!As a Data Engineer at Honeywell, you will be accountable for guiding the design, development, and implementation of data engineering solutions, ensuring the effective collection, processing, and analysis of data to facilitate business insights and drive innovation.Key Skills and Qualifications

• Extensive hands-on experience in building data pipelines using large-scale distributed data processing tools and frameworks, including Python, Spark, and Databricks, along with strong expertise in PySpark and Scala.
• Solid knowledge of data management concepts such as data modeling, CDC, ETL/ELT processes, data lakes, and effective data governance practices, coupled with a strong implementation of data quality for AI training datasets.
• Proficient in utilizing cloud platforms like Azure, GCP, and Databricks to implement AI/ML solutions and experienced in developing RAG architectures and LLM-powered applications.
• Expertise in real-time data processing frameworks including Kafka, Apache Spark Streaming, and Structured Streaming, alongside experience in building data pipelines for AI model deployment with a familiarity in MLOps practices.
• Strong background in containerization using Docker and orchestration with Kubernetes, knowledge of time-series databases and IoT data modeling patterns, along with experience in graph databases and working in distributed teams with Agile and Scrum methodologies.

Our Offer

• A culture that fosters inclusion, diversity, and innovation in an international work environment
• Market specific training and ongoing personal development.
• Experienced leaders to support your professional development

We are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Join us now and be part of a global team of thinkers, innovators, dreamers, and doers who make the things that make the future!

#TheFutureIsWhatWeMakeItKey Responsibilities

Data Engineering & AI Pipeline Development:

• Design and implement scalable data architectures and robust data integration solutions to process high-volume IoT sensor data and telemetry streams, facilitating the AI product lifecycle through optimized data pipelines, feature engineering, and efficient retrieval mechanisms for AI/ML workloads.

DataOps & Governance:

• Define and implement mature DataOps strategies to ensure continuous integration and delivery of data pipelines for AI solutions, while leading efforts in data quality, observability, and lineage tracking to maintain dataset integrity.
• Create self-service data assets for data scientists and ML engineers, design automated documentation systems for data lineage and AI model provenance, and ensure compliance with data governance policies related to security, privacy, and regulatory requirements.

Technical Leadership & Innovation:

• Lead architectural discussions to establish standards and drive technical excellence across teams, while partnering with ML engineers and data scientists to implement efficient data workflows for model training, fine-tuning, and deployment.
• Mentor data engineers on best practices and innovative approaches, promote continuous improvement in data engineering practices and tooling, and manage stakeholder expectations by aligning data engineering roadmaps with business and AI strategy.

About Us

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments - powered by our Honeywell Forge software - that help make the world smarter, safer and more sustainable.