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Summary:
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 is a high-impact, high-expectation role requiring deep technical expertise in Databricks and a proven ability to deliver production-grade data solutions at scale. We 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.
Why This Role Matters
You will play a critical role in shaping the organization’s data platform and enabling data-driven decision-making. Your work will directly impact how data is trusted, accessed, and used across the enterprise. This role is adecuado for an engineer who thrives on hands-on ownership, sets a high bar for engineering excellence, and is driven to design, build, and operate robust, scalable data systems that deliver meaningful impact across the enterpris
Essential Duties and Responsibilities:
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, scalability, and cost efficiency
Diagnose and resolve complex data pipeline failures
Ensure data assets are discoverable and well-documented
Implement secure data access patterns and governance
Ensure compliance with data governance and regulatory requirements
Collaborate with cross-functional teams to translate requirements into scalable data solutions
Mentor engineers and contribute to a high-performance, collaborative engineering culture
Contribute to the evolution of the data platform to improve scalability, usability, and engineering efficiency
Stay current with advancements in data engineering and Databricks
Qualifications:




Bachelor’s Degree in Computer Science, Information Systems, or a related field.
8+ years in Data Engineering and Data Architecture focused, with hands-on experience delivering solutions utilizing Databricks (preferably on Azure). Must have a strong understanding of cloud-based data solutions or equivalent combination of education and 8+ years’ experience required.
Required Knowledge, Skills, and Abilities
Deep, hands-on experience designing, building, and optimizing data pipelines and data products within Azure Databricks environments
Databricks must be the primary platform used in recent roles, not incidental or secondary experience
Proven experience optimizing Databricks workloads and overall platform usage for performance, scalability, and cost efficiency
Strong proficiency in PySpark and Spark within Databricks, including performance tuning
Strong understanding of Medallion Architecture implemented within Databricks
Experience building and operating production-grade pipelines within Databricks
Experience with Databricks-native ingestion frameworks
Experience implementing CI/CD pipelines within Databricks
Strong troubleshooting skills in distributed systems, Strong communication skills
Demonstrated ability to evaluate and make sound tradeoffs across performance, scalability, cost, maintainability, and time-to-delivery
Preferred Knowledge, Skills, and Abilities
Experience with Databricks Genie
Experience with AI-assisted data engineering tools
Experience with enterprise data platforms
Experience collaborating with BI teams
Knowledge of governance and security
Familiarity with Infrastructure as Code, Relevant cloud certifications
Azure DevOps experience for work tracking
Success Traits for this Role
Strong ownership mindset with accountability for end-to-end delivery and production
reliability
Ability to evaluate tradeoffs across performance, scalability, and maintainability
Ability to make high-quality decisions in ambiguous environments
Excellent communication and collaboration skills
Strong bias toward execution and delivery
Commitment to continuous improvement and operational excellence
Experience with incident response and root cause analysis
High learning agility and adaptability
Commitment to building and supporting resilient data systems across team boundaries
Set and uphold a high bar for code quality, performance, and reliability

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📌 Data Engineer (Ejido de la Finca)
🏢 Daikin Manufacturing México
📍 Ejido de la Finca