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.
YOU MUST HAVEAdvanced Skill Requirement Experience & Capabilities8–12 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 SQLBig 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 / ADLSBigQueryRedshiftEmerging & 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
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 warehouseDatabricks 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 FactoryAWS 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