The Sr Advanced Data Engineer – AI‑Ready Data Platforms is responsible for architecting, building, and optimizing large‑scale data systems that power Honeywell Aerospace’s enterprise data strategy and AI‑ready data layer.

This role plays a critical part in ensuring that the organization’s data platforms are scalable, governed, performant, and aligned to AI and advanced analytics use cases. The Sr Advanced Data Engineer partners closely with AI/ML teams, data scientists, platform teams, and business stakeholders to ensure that data is available, trusted, and production‑ready to support analytics, advanced analytics, and AI initiatives in a timely manner.

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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.

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As a Senior Advanced Data Engineer here at Honeywell, you will design, develop, and optimize data pipelines and architectures, ensuring efficient data handling and analysis to drive data-driven decision-making and operational efficiency.

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YOU MUST HAVE

Advanced Skill Requirement

Experience & Capabilities

• 8–12 years of experience in data engineering or advanced data platform roles
• Proven experience designing and operating enterprise‑scale data platforms
• Strong hands‑on experience building AI‑ready, governed, and automated data layers
• Experience working in large, global, and regulated enterprise environments

Advanced Skill Requirements

Core Languages

• Expert proficiency in Python and SQL

Big Data & Analytics Platforms

• Deep experience with:
  • Snowflake (enterprise data warehouse)
  • Databricks (analytical data lake platforms)
• Strong understanding of distributed data processing concepts

Cloud Platforms

• Hands‑on experience with AWS, Azure, and/or Google Cloud Platform (GCP), including services such as:
  • S3 / ADLS
  • BigQuery
  • Redshift

Emerging & Advanced Technologies

• Familiarity with Vector Databases to support AI and LLM use cases
• Experience implementing CI/CD pipelines for data engineering workloads

Education

• Bachelor’s or Master’s degree in Engineering, Computer Science, Information Technology, Data Engineering, or a related field

Who Will Succeed in This Role

• Experienced data engineers who can design, build, and scale enterprise data platforms
• Professionals who ensure the data layer is robust, governed, automated, and AI‑ready
• Engineers with strong focus on performance, accuracy, reliability, and compliance
• Individuals who can support analytics, advanced analytics, and AI applications with high‑quality, trusted data

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Key Responsibilities

Architecture & System Design

• Design and own end‑to‑end, scalable enterprise data architectures, including:
  • Data Lake
  • Data Mesh
  • Medallion (Bronze / Silver / Gold) architectures
• Align data architecture decisions with long‑term business goals and AI strategy
• Select, evaluate, and standardize the enterprise data technology stack, including:
  • Cloud‑native data services
  • Snowflake enterprise data warehouse
  • Databricks analytical data lake platforms
• Actively participate in AI initiatives, ensuring the data layer is AI‑ready and fit for enterprise AI consumption

Pipeline & Infrastructure Development

• Build, manage, and optimize complex ETL / ELT pipelines using tools such as:
  • Apache Airflow
  • Azure Data Factory
  • AWS Glue
  • Informatica
• Design and implement real‑time and near‑real‑time data pipelines using:
  • Apache Kafka
  • Spark Structured Streaming
• Establish standardized data ingestion and transformation pipelines across enterprise systems
• Ensure high‑quality, timely availability of data for analytics, advanced analytics, and AI use cases

Performance Tuning & Optimization

• Identify and resolve performance bottlenecks in distributed data systems
• Optimize query performance, processing latency, and cloud costs through:
  • Partitioning strategies
  • Clustering
  • Indexing
• Work closely with data platform and cloud teams to ensure adoption of latest data technologies and optimizations

Data Governance, Quality & Observability

• Define and enforce enterprise data quality standards using frameworks such as Great Expectations
• Implement and support data governance, lineage, and observability tools
• Ensure compliance with global data regulations (e.g., GDPR, CCPA) by implementing:
  • Data encryption
  • Role‑Based Access Control (RBAC)
• Maintain strong guardrails for data usage, access, and quality across the enterprise

Leadership, Collaboration & Mentorship

• Provide technical leadership and guidance to junior and mid‑level data engineers
• Conduct code reviews and promote best practices in documentation and data engineering standards
• Act as a technical bridge between leadership, data scientists, AI teams, and business stakeholders
• Translate business and AI requirements into actionable, scalable data solutions