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Remote Databricks Data Engineer Jobs in Arizona (NOW HIRING)

Paychex

Data Solutions Engineer

Tempe, AZ · On-site +1

$91K - $156K/yr

Overview The Data Solutions Engineer will play a key role in integrating, architecting, and ... Databricks, Snowflake, and Informatica IDMC. Develop automation frameworks and CI/CD pipelines ...

Insight

You will have the flexibility to work fully remote from anywhere across the US. Insight at a Glance ... Databricks: ML Associate, Data Engineer Associate, Generative AI Engineer Associate Who You Are * A ...

Insight

$133K - $178K/yr

You will have the flexibility to work fully remote from anywhere across the US. Insight at a Glance ... Databricks: ML Associate, Data Engineer Associate, Generative AI Engineer Associate Who You Are * A ...

Koantek

Resource Manager

Chandler, AZ · Remote

Remote (Coverage for EST & PST required) Role Overview As the Resource Manager for Koantek ... Familiarity with the Databricks ecosystem or similar data engineering platforms.

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Remote Databricks Data Engineer information

What are the key skills and qualifications needed to thrive as a Remote Databricks Data Engineer, and why are they important?

To thrive as a Remote Databricks Data Engineer, you need a solid background in data engineering, strong programming skills in Python or Scala, and experience with big data frameworks, often supported by a degree in computer science or a related field. Proficiency with Databricks, Apache Spark, cloud platforms (such as AWS or Azure), and relevant certifications like Databricks Certified Data Engineer are highly valuable. Strong problem-solving abilities, effective remote communication, and collaboration skills set top performers apart in distributed teams. These skills and qualities ensure efficient data pipeline development, seamless integration, and successful project delivery in remote environments.

What is a Remote Databricks Data Engineer?

A Remote Databricks Data Engineer is a professional who designs, develops, and manages large-scale data processing systems using the Databricks platform, often working from a remote location. They focus on building data pipelines, integrating data sources, and optimizing workflows for analytics and machine learning, leveraging tools like Apache Spark within Databricks. These engineers collaborate with data scientists, analysts, and other stakeholders to ensure data is accessible, reliable, and scalable for business needs. Remote roles offer flexibility in work location while still requiring strong communication and technical skills.

What are some common challenges faced by remote Databricks Data Engineers and how can they be addressed?

Remote Databricks Data Engineers often encounter challenges such as coordinating efficiently with distributed teams, managing access to secure data environments, and ensuring smooth pipeline deployments across different cloud platforms. To overcome these, it's important to leverage communication tools for regular check-ins, follow strict data governance protocols, and utilize collaborative features in Databricks such as shared notebooks and version control. Proactively documenting your work and staying updated with platform updates can also help streamline remote collaboration and problem-solving.
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Remote Databricks Data Engineer jobs near you

AI Engineer - GenAI, MLOps & Cloud Platforms - AIRLHV

NavitasPartners

Tucson, AZ • On-site, Remote

$52.75 - $70.50/hr

Other

Posted 22 days ago

Job description

AI Engineer - GenAI, MLOps & Cloud Platforms 

Location: US / Canada (Remote/Hybrid) 
Type: Contract / Full-Time 

Overview: 

Join a high-impact team delivering enterprise AI solutions. This role emphasizes building intelligent systems using modern ML, GenAI, and cloud-native technologies. 

Key Responsibilities: 

  • Develop and operationalize machine learning and GenAI models  
  • Build scalable data and model pipelines using cloud technologies  
  • Partner with cross-functional teams to deliver AI-driven insights  
  • Ensure scalability, performance, and governance of AI systems  

Required Skills: 

  • Hands-on experience in ML engineering, MLOps, and model lifecycle management  
  • Strong programming skills in Python and ML frameworks  
  • Experience with cloud ecosystems (AWS, Azure, GCP)  
  • Knowledge of distributed data processing and integration  

Nice to Have / Coverage: 

  • Experience with Databricks, Snowflake, or BigQuery for data engineering workflows  
  • Familiarity with LangChain and agent-based AI systems  
  • Exposure to enterprise AI governance and compliance standards  
  • Experience collaborating with cloud/data platform architects  

For more details reach at resumes@navitassols.com.

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