Job description
as a senior advanced data engineer 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 build a strong, diverse team by recruiting talent, identifying and developing successors, driving retention and engagement, and fostering an inclusive culture.
responsibilities
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.
qualifications
you must have
* databricks: 4+ years hands‑on experience with 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 (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.
-hybrid
about us
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.
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