We are looking for a Senior Advanced Data Engineer with 9+ years of experience to architect, build, and scale cloud‑native data and AI platforms on Azure using Databricks. This is a senior, hands‑on technical leadership role requiring deep expertise in data engineering, lakehouse architecture, and AI/ML data pipelines to enable advanced analytics, machine learning, and business intelligence use cases.
The ideal candidate will lead complex, enterprise‑scale data initiatives, work closely with data scientists and ML engineers, and play a critical role in shaping the organization’s data and AI strategy.
Responsibilities- Architect, design, and lead the development of end‑to‑end data pipelines on Azure using Databricks (Spark / PySpark)
- Own the design and evolution of lakehouse architecture using Azure Data Lake Storage (ADLS Gen2) and Delta Lake
- Build, optimize, and scale batch and streaming pipelines for large‑volume, high‑velocity datasets
- Design and manage feature engineering pipelines and curated datasets for AI/ML model training, validation, and inference
- Partner closely with Data Scientists and ML Engineers to enable scalable, production‑ready ML workflows
- Support and integrate with MLOps pipelines, including:
- Data and feature versioning
- Feature stores
- Model deployment readiness
- Lead optimization of Databricks workloads for performance, scalability, reliability, and cost efficiency
- Define and implement data quality, validation, monitoring, and observability frameworks
- Enforce data security, governance, and compliance using Azure and Databricks best practices
- Review designs and code, establish engineering standards, and ensure platform reliability
- Mentor and technically guide senior, mid‑level, and junior data engineers
- Lead architectural decision‑making and contribute to long‑term data platform and AI roadmap planning
- Act as a technical authority and escalation point for complex data engineering challenges
Required Skills & Qualifications
- 9+ years of hands‑on experience in Data Engineering, Data Platform, or Big Data roles
- Deep expertise in Python, PySpark, and Spark SQL
- Extensive, real‑world experience with Databricks, including:
- Jobs, notebooks, workflows
- Delta Live Tables
- Performance tuning and job orchestration
- Strong experience with Azure cloud services, including:
- Azure Data Lake Storage (ADLS Gen2)
- Azure Databricks
- Azure Data Factory and/or Synapse Pipelines
- Expert‑level understanding of Delta Lake, including ACID guarantees, schema enforcement, and optimizations
- Advanced SQL skills for analytical data modeling and transformations
- Proven experience designing AI/ML data pipelines (training, validation, inference datasets)
- Strong understanding of lakehouse, data warehousing, and dimensional modeling concepts
- Hands‑on experience with CI/CD pipelines, Git, and DevOps practices for data platforms
- Excellent troubleshooting, diagnostics, and performance tuning skills
- Strong communication and stakeholder collaboration abilities
Preferred / Nice to Have Skills
- Experience with Azure Machine Learning or Databricks ML
- Hands‑on experience with Feature Store, MLflow, or experiment tracking frameworks
- Streaming data experience using Kafka, Azure Event Hubs, or Spark Structured Streaming
- Experience with dbt, Unity Catalog, or enterprise data governance tools
- Familiarity with Power BI or other BI/visualization tools
- Strong exposure to production‑grade MLOps systems and best practices
- Prior experience as a Technical Lead, Principal Engineer, or Architecture Owner
- Knowledge on LangChain , Agent, Agent Architecture.
Skills Required
- 10+ years of experience in Data Engineering, ETL Development, Database Administration
- Strong hands-on experience with Databricks in production environments
- Advanced SQL skills and solid expertise in data modeling
- Proficiency in Python, SQL, and PySpark
- Strong experience with Apache Spark and PySpark
- Experience with Delta Lake, schema evolution, and data versioning
- Experience with cloud platforms (AWS, Azure, or GCP)
- Experience building scalable, reliable, fault-tolerant data pipelines
- Solid understanding of distributed data systems
- Experience with streaming platforms (Kafka)
- Exposure to ML pipelines or feature stores (Databricks Feature Store preferred)
Honeywell Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Honeywell and has not been reviewed or approved by Honeywell.
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Retirement Support — Retirement plans feature a notably strong company 401(k) match with vesting after three years, enhancing long-term savings security. Additional tax-advantaged accounts and company contributions for eligible earners further strengthen financial preparedness.
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Leave & Time Off Breadth — Time off policies include flexible or unlimited vacation for many salaried roles and a broad observed-holiday schedule, providing manager-approved flexibility. This structure supports rest and work-life balance across varied needs.
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Parental & Family Support — Parental leave offers paid time for birth, adoption, or foster care that can be taken consecutively or intermittently. The design enables practical flexibility in how family leave is used.
Honeywell Insights
What We Do
Honeywell is a Fortune 500 company that invents and manufactures technologies to address tough challenges linked to global macrotrends such as safety, security, and energy. With approximately 110,000 employees worldwide, including more than 19,000 engineers and scientists, we have an unrelenting focus on quality, delivery, value, and technology in everything we make and do.
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