Data Engineer

Make your mark at the world's largest HVAC company! Why You’ll Love Working for Daikin:
We’re a group who operates with a people-centered mindset, believing in the unlimited potential of each employee, with a goal of driving our company to its fullest potential. Our goal is to “Perfect the Air”. We do this through cutting edge innovation & sustainability efforts while meeting the HVAC needs of thousands of customers - expanding over 160 countries! Our culture is unrivaled.

Daikin operates under 8 Core Values which are embedded into every aspect of our business. It’s through these values that we continuously strive to build a place where everyone can be authentic, feel valued and heard, and have a sense of purpose and belonging.

Daikin Applied Americas is seeking a Senior Data Engineer with deep, hands‑on expertise in Azure Databricks as a primary data engineering platform to design, build, and operate scalable, production‑grade data pipelines and data products. This role is responsible for ensuring the availability, reliability, and quality of data across the organization. This role operates as part of a unified data platform team, working in close alignment with data visualization engineers, analytics teams, and data product owners to deliver cohesive, scalable, and high‑quality data products.

You will collaborate with cross‑functional teams to deliver data solutions that power analytics, business intelligence, machine learning, and emerging AI‑driven experiences leveraging Databricks capabilities such as Genie and related AI‑assisted tooling. This role requires strong ownership, a design‑first mindset, and a bias toward delivering reliable, production‑ready data solutions in complex environments.

This role is a high‑impact, high‑expectation role requiring deep technical expertise in Databricks and a proven ability to deliver production‑grade data solutions  are seeking engineers who operate at a high level of ownership, consistently deliver high‑quality work, and contribute to raising the bar for engineering excellence across the team. This role operates at a senior level and requires the ability to:

• Make and own architectural decisions in ambiguous environments
• Define and drive engineering standards and design patterns
• Influence technical direction across teams
• Mentor and elevate other engineers
• Balance short‑term delivery with long‑term platform health

This role emphasizes production excellence, requiring ownership of reliability, observability, and continuous improvement of data systems at scale, as well as defining and enforcing strong design patterns.

• Design, build, and operate scalable, production‑grade data products with a focus on usability, discoverability, and alignment with business outcomes
• Own core data platform architecture, including data pipelines, storage, and foundational data models within Databricks Architect, implement, and continuously evolve data models and curation patterns (e.g., Medallion Architecture)
• Own data solutions end‑to‑end in production, including design, development, deployment, monitoring, incident response, root cause analysis, and continuous improvement
• Design and operate data solutions that handle large‑scale, high‑volume, and complex data workloads with a focus on reliability and performance
• Maintain a strong hands‑on focus, actively contributing to the design, development, and operation of data solutions in production environments
• Lead technical decision‑making for complex data engineering initiatives, taking ownership of design choices and their long‑term impact on the platform
• Collaborate with data visualization and analytics engineers to ensure appropriate placement of data transformations, business logic, and aggregations
• Collaborate across engineering roles to ensure end‑to‑end data quality, consistency, and reliability
• Leverage AI‑based tools and techniques to accelerate delivery and improve engineering productivity
• Design, build, and operate data quality, validation, and observability frameworks
• Design and implement testing strategies for data pipelines
• Optimize data pipelines and platform usage for performance,…