We are looking for a Senior Data Engineer with 6+ years of experience to design, build, and scale cloud‑native data and AI platforms on Azure using Databricks. The role requires strong hands‑on expertise in data engineering, lakehouse architecture, and AI/ML data pipelines to support advanced analytics, machine learning, and business intelligence use cases.
The ideal candidate will lead complex data initiatives, collaborate closely with data scientists and ML engineers, and play a key role in shaping the organization’s data and AI strategy.
ResponsibilitiesKey Responsibilities
- Architect and develop end‑to‑end data pipelines on Azure using Databricks (Spark / PySpark)
- Design and maintain lakehouse architectures using Azure Data Lake + Delta Lake
- Build and optimize batch and streaming pipelines for large‑scale datasets
- Create and manage feature pipelines and curated datasets for AI/ML model training and inference
- Collaborate with data scientists and ML engineers to enable scalable ML workflows
- Support MLOps pipelines, including data versioning, feature stores, and model deployment readiness
- Optimize Databricks workloads for performance, scalability, and cost efficiency
- Implement data quality, validation, monitoring, and observability frameworks
- Ensure data security, governance, and compliance using Azure and Databricks best practices
- Review code, define standards, and mentor junior and mid‑level data engineers
- Lead architectural decisions and contribute to data platform roadmap planning
Required Skills & Qualifications
- 6+ years of hands‑on experience in Data Engineering or Data Platform roles
- Strong proficiency in Python, PySpark, and Spark SQL
- Extensive experience with Databricks (jobs, notebooks, workflows, Delta Live Tables)
- Strong experience with Azure Cloud services, including:
- Azure Data Lake Storage (ADLS Gen2)
- Azure Databricks
- Azure Data Factory / Synapse Pipelines
- Solid understanding of Delta Lake, including optimization and ACID guarantees
- Advanced SQL skills for analytical data modeling
- Experience designing AI/ML data pipelines (training, validation, inference datasets)
- Knowledge of data warehousing, lakehouse, and dimensional modeling concepts
- Familiarity with CI/CD, Git, and DevOps practices
- Strong troubleshooting, performance tuning, and problem‑solving skills
- Knowledge on LangChain , Agent, Agent Architecture.
Preferred / Nice‑to‑Have Skills
- Experience with ML platforms such as Azure Machine Learning or Databricks ML
- Hands‑on experience with Feature Store, MLflow, or experiment tracking
- Streaming data experience (Kafka, Event Hubs, Spark Structured Streaming)
- Experience with dbt, Unity Catalog, or data governance tools
- Knowledge of BI and visualization tools (Power BI preferred)
- Exposure to MLOps best practices and production ML systems
- Prior experience as a technical lead or mentor
About UsHoneywell 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.
Skills Required
- 6+ 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, PySpark
- Strong experience with Apache Spark and PySpark
- Experience working 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.
