Summary: As a Senior Advanced Data Engineer, you will design, develop, and maintain advanced data solutions to drive business insights and support decision-making processes. Highlights: 1. Design and implement advanced data solutions for business insights 2. Build scalable data pipelines and optimize data storage 3. Enable data-driven decision-making and improve data management 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. **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 * ***\#LI*** *\-Hybrid* **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 manage Databricks Workflows with multi\-task dependencies, SLA monitoring, retry logic, and alerting * Implement CI/CD pipelines for Databricks using Azure DevOps and GitHub Actions : including Python Wheel packaging for reusable utility libraries deployed across the platform * Apply software 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 manage Unity Catalog for centralized data governance: three\-level namespace (catalog schema* table), fine\-grained RBAC, data masking, and audit logging * Build data quality frameworks: rule\-based validation, deduplication, reconciliation, and anomaly detection: ensuring data arrives fit for AI/ML consumption * Establish data lineage tracking across ingestion, transformation, and serving layers * Govern data delivery to GCP: ensuring secure, validated, schema\-consistent outputs consumed by downstream data science and analytics teams **AI \& Proactive Analytics Foundation** * Design pipelines that are AI\-ready from day one: supporting structured ML feature pipelines, embedding generation, and future Vector DB integrations * Build the data infrastructure that enables the shift from descriptive dashboards to proactive, predictive analytics * Collaborate with Data Scientists and Analytics Engineers to ensure the Gold layer supports model training, feature stores, and real\-time inference pipelines 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.
Juan García
Indeed · HR
Juan García
Indeed · HR
