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Databricks Engineer Jobs in Arizona (NOW HIRING)

Software Engineer I

Chandler, AZ · On-site

$100 - $125/hr

Databricks & Lakehouse Engineering * Design and optimize bronze, silver, and gold pipelines using Delta Lake. * Develop scalable Spark workloads for large datasets. * Manage governance, access ...

Senior Data Platform Engineer

Tempe, AZ · On-site

$109K - $131K/yr

We are seeking a seasoned Databricks Data Engineer with expertise in Azure cloud services and the Databricks Lakehouse platform. The role involves designing and optimizing large-scale data pipelines ...

Big Data Engineer with Java

Phoenix, AZ · On-site

$54.25 - $71.75/hr

Hadoop echo system, Spark, Databricks, and Azure-based services * Experience with medium to large ... The Data Engineer II is responsible for partnering with business unit leaders within NCR to analyze ...

Experience with Databricks Apps and the broader Databricks platform is highly desirable but not required. Key Responsibilities Front-End & Application Engineering Design, develop, and deploy modern ...

Experience with Databricks Apps and the broader Databricks platform is highly desirable but not required. Key Responsibilities Front-End & Application Engineering * Design, develop, and deploy modern ...

Experience with Databricks Apps and the broader Databricks platform is highly desirable but not required. Key Responsibilities Front-End & Application Engineering Design, develop, and deploy modern ...

Databricks: Expert-level administration; Unity Catalog, cluster policies, SQL warehouses, identity ... Mentor engineers, analysts, and business users on getting the most out of the platform What We're ...

The ideal candidate will possess deep architectural knowledge of Azure Data Lake and Databricks, a ... Azure certifications, such as Azure Data Engineer Associate or Azure Solutions Architect Expert.

Showing results 21-40

Databricks Engineer information

See Arizona salary details

$55.4K

$104K

$189.2K

How much do databricks engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for databricks engineer in Arizona is $104,028.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $123,500.00 per year, depending on experience, location, and employer.

What is a Databricks engineer?

A Databricks Engineer is a data engineering professional who specializes in using the Databricks platform to build, manage, and optimize data pipelines and analytics solutions. They work with big data technologies like Apache Spark, Delta Lake, and cloud services to process and analyze large datasets efficiently. Their role often involves developing ETL (extract, transform, load) workflows, setting up data lakes, and ensuring data quality and performance for business intelligence and machine learning applications.

What are the key skills and qualifications needed to thrive as a Databricks engineer?

To thrive as a Databricks Engineer, you need strong expertise in big data processing, cloud platforms (like AWS or Azure), and proficiency with languages such as Python, SQL, and Scala, often supported by a degree in computer science or a related field. Familiarity with Apache Spark, Databricks Workspace, version control systems like Git, and relevant Databricks certifications are typically required. Strong analytical thinking, collaboration, and effective communication skills help you understand business needs and work seamlessly with data teams. These skills ensure efficient data pipeline development, scalable analytics solutions, and successful integration of Databricks into organizational workflows.

What are some common challenges faced by Databricks engineers when working with large-scale data pipelines?

Databricks Engineers often encounter challenges related to optimizing the performance and reliability of large-scale data pipelines. These can include efficiently managing cluster resources, handling data partitioning to prevent bottlenecks, and troubleshooting job failures due to resource constraints or data quality issues. Collaboration with data scientists, analysts, and DevOps teams is essential to ensure seamless integration and deployment of production workflows. Staying current with evolving Databricks features and best practices also plays a key role in overcoming these challenges.

How much does a Databricks engineer make?

A Databricks engineer's salary typically ranges from $90,000 to $150,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized skills in Spark, cloud platforms, and data engineering can earn higher compensation, often including bonuses and benefits.

Is a Databricks engineer in demand?

Databricks engineers are in high demand due to the growing adoption of cloud-based data analytics and machine learning platforms. They typically require skills in Spark, SQL, and cloud environments like AWS or Azure, making them valuable in data-driven organizations across various industries.

What cities in Arizona are hiring for Databricks Engineer jobs?

Cities in Arizona with the most Databricks Engineer job openings:

Infographic showing various Databricks Engineer job openings in Arizona as of September 2026, with employment types broken down into 1% Internship, 87% Full Time, 7% Part Time, 4% Contract, and 1% Nights. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $104,028 per year, or $50 per hour.

Software Engineer I

Chandler, AZ • On-site

$100 - $125/hr

Other

Posted 6 days ago


Job description

Are you looking for a unique opportunity to be a part of something great? Want to join a 17,000-member team that works on the technology that powers the world around us? Looking for an atmosphere of trust, empowerment, respect, diversity, and communication? How about an opportunity to own a piece of a multi-billion dollar (with a B!) global organization? We offer all that and more at Microchip Technology Inc.

People come to work at Microchip because we help design the technology that runs the world. They stay because our culture supports their growth and stability. They are challenged and driven by an incredible array of products and solutions with unlimited career potential. Microchip’s nationally-recognized Leadership Passage Programs support career growth where we proudly enroll over a thousand people annually. We take pride in our commitment to employee development, values-based decision making, and strong sense of community, driven by our Vision, Mission, and 11 Guiding Values; we affectionately refer to it as the Aggregate System and it’s won us countless awards for diversity and workplace excellence.

Our company is built by dedicated team players who love to challenge the status quo; we did not achieve record revenue and over 30 years of quarterly profitability without a great team dedicated to empowering innovation. People like you.

Visit our careers page to see what exciting opportunities and company perks await!

Job Description: Role Overview

We are seeking a Software Engineer with hands-on experience building and operating AI and data pipelines in a modern lakehouse environment. This role combines applied machine learning, data engineering, and analytics engineering, with strong emphasis on dbt Core, SQL, Databricks, and AWS.

You will own AI workflows end-to-end — from governed feature modeling through training, deployment, monitoring, and performance optimization — in a production environment.

This is a delivery-oriented role. You are expected to design solutions, write production-quality code, and improve platform reliability.

Core Responsibilities AI & Machine Learning Engineering
  • Design and implement machine learning training and batch inference pipelines on Databricks using Spark and MLflow.
  • Develop and deploy ML models (classification, regression, forecasting, or NLP-based workloads).
  • Implement LLM-enabled solutions, including embedding pipelines, semantic search systems, and retrieval-augmented generation (RAG) workflows.
  • Proven experience designing feature engineering pipelines that ensure consistency between offline training datasets and production inference workflows.
  • Manage model lifecycle, including versioning, experiment tracking, evaluation, and deployment.
  • Implement monitoring for model drift, data drift, and prediction stability.
  • Diagnose and resolve production failures across training and inference pipelines.
dbt Core & Data Contract Ownership
  • Architect and maintain dbt Core projects that serve as the transformation and contract layer for AI workloads.
  • Design incremental models, snapshots, and historical datasets to support reproducible training.
  • Enforce data contracts and schema stability for downstream ML pipelines.
  • Implement automated testing, freshness validation, and CI pipelines for dbt projects.
  • Collaborate with data engineers to ensure AI systems consume governed, high-quality datasets.
Databricks & Lakehouse Engineering
  • Design and optimize bronze, silver, and gold pipelines using Delta Lake.
  • Develop scalable Spark workloads for large datasets.
  • Manage governance, access control, and lineage using Unity Catalog.
  • Optimize compute performance and cost efficiency for AI workloads.
  • Contribute to architectural decisions across data and AI infrastructure.
AWS Cloud Engineering
  • Build and operate AI workflows in an AWS-based lakehouse environment.
  • Work with Amazon S3, IAM roles and policies, and related AWS services supporting data pipelines.
  • Design secure and scalable data access patterns across environments.
  • Support deployment of models using containerized or managed services when required.
  • Monitor and optimize cloud resource utilization.
Engineering Excellence & Reliability
  • Implement CI/CD pipelines for dbt projects, ML code, and infrastructure.
  • Write maintainable Python and SQL with appropriate documentation and testing.
  • Conduct code reviews and provide guidance to junior engineers.
  • Ensure production systems meet reliability, auditability, and governance standards.
  • Partner with analytics and business stakeholders to translate requirements into scalable AI solutions.
Requirements/Qualifications: Required Qualifications
  • 0-3 years of total professional experience.
  • Professional experience in AI, ML, data engineering, or analytics engineering roles.
  • Strong hands-on experience with dbt Core in production environments.
  • Advanced SQL skills with performance tuning experience.
  • Proven experience with Databricks (Spark, Delta Lake, MLflow).
  • Experience building AI/ML pipelines on AWS.
  • Proficiency in Python for ML and data processing workflows.
  • Experience managing AI workloads end-to-end, including deployment and monitoring.
Preferred Qualifications
  • Experience working with centralized, versioned feature datasets.
  • Exposure to vector databases or LLM-based systems.
  • Experience implementing model monitoring and drift detection frameworks.
  • Experience supporting near-real-time AI systems.
Travel Time:

0% - 25%

Physical Attributes:

Hearing, Seeing, Talking, Works Alone, Works Around Others

Physical Requirements:

80% sitting, 10% standing, 10% walking, 100% inside.

Microchip Technology Inc is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.

For more information on applicable equal employment regulations, please refer to the Know Your Rights: Workplace Discrimination is Illegal Poster.

To all recruitment agencies: Microchip Technology Inc. does not accept unsolicited agency resumes. Please do not forward resumes to our recruiting team or other Microchip employees. Microchip is not responsible for any fees related to unsolicited resumes.

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