Sr Advanced AI Data Engineer

As a Senior Advanced Data Engineer here at Honeywell you will play a crucial role in designing developing and maintaining advanced data solutions that drive business insights and support decision-making processes. You will leverage your expertise in data engineering to build scalable data pipelines optimize data storage and ensure data quality and integrity.

Your ability to work with cross-functional teams and translate business requirements into technical solutions will be key to your success in this role.

In this role you will impact the business by enabling data-driven decision-making optimizing data processes and improving overall data management. Your work will contribute to increased operational efficiency cost savings and enhanced customer satisfaction.

At Honeywell our people leaders play a critical role in developing and supporting our employees to help them perform at their best and drive change across the company. Help to build a strong diverse team by recruiting talent identifying and developing successors driving retention and engagement and fostering an inclusive culture.

AI-Ready Data Platform

• Design and implement end-to-end ingestion pipelines from heterogeneous sources: including Snowflake SQL Server Excel REST APIs and unstructured data: into Azure Databricks
• Architect and enforce Medallion Architecture (Bronze Silver Gold) ensuring data arrives clean validated and fit for purpose at each layer
• Build Delta Live Tables (DLT) pipelines with declarative data quality expectations schema evolution and automated lineage tracking
• Implement incremental loading patterns using CDC (Change Data Capture) watermarking and Delta Lake MERGE/UPSERT for efficient scalable ingestion
• Enable structured and unstructured data processing: documents Excel files JSON Parquet : building the foundation for AI and ML consumption

Data Modeling & Semantic Layer

• Design and implement the Engineering data model: dimensional models fact/dimension tables and domain-specific data marts: serving analytics BI ML and AI use cases
• Build a governed reusable semantic layer on top of the Gold layer enabling self-service analytics through Power BI and GCP-connected consumers
• Ensure data models are documented versioned and aligned to business domains within the VECE COE

Orchestration and Data Ops

• Build and manageDatabricks Workflowswith multi-task dependencies SLA monitoring retry logic and alerting
• ImplementCI/CD pipelinesfor Databricks using Azure DevOps and GitHub Actions : includingPython Wheel packagingfor reusable utility libraries deployed across the platform
• Applysoftware engineering best practices: version control unit testing modular code design and automated deployment to Dev/QA/Prod environments
• Cluster right-sizing DBU management Delta table optimization (VACUUM compaction) cost monitoring across Azure Databricks and GCP

Data Governance & Quality

• Implement and manageUnity Catalogfor centralized data governance: three-level namespace (catalog schema table) fine-grained RBAC data masking and audit logging
• Builddata quality frameworks: rule-based validation deduplication reconciliation and anomaly detection: ensuring data arrives fit for AI/ML consumption
• Establishdata lineage trackingacross ingestion transformation and serving layers
• Govern data delivery toGCP: ensuring secure validated schema-consistent outputs consumed by downstream data science and analytics teams

AI & Proactive Analytics Foundation

• Design pipelines that areAI-ready from day one: supporting structured ML feature pipelines embedding generation and futureVector DBintegrations
• Build the data infrastructure that enables the shift fromdescriptive dashboards to proactive predictive analytics
• Collaborate with Data Scientists and Analytics Engineers to ensure the Gold layer supportsmodel training feature stores and real-time inference pipelines

YOU MUST HAVE

• Databricks: 4 years hands-on: PySpark Delta Lake Workflows Unity Catalog.
• Demonstrate expertise in data strategy for example: Medallion Architecture Domain Data Modeling and Functional Data Architecture.
• Data Quality Frameworks (i.e. rule-based validation anomaly detection)
• Data Pipelines: incremental loading CDC CI/CD Observability
• Advanced Python/Pyspark and Advanced SQL
• Strongly preferred: DLT UC GCP Azure Kafka.
• Highly value Databricks Certified Professional
• 7 years of overall data engineering experience
• 4 years of hands-on Azure Databricks experience in production environments
• Proven experience building platforms not just maintaining them: greenfield builds migrations framework development
• Experience with financial engineering enterprise or industrial-scale datasets preferred
• Demonstrated ability to own technical decisions end-to-end: from architecture to production deployment

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