2

Remote Databricks Developer Jobs in Chandler, AZ

Remote Position Type: Contract Job Overview We are seeking an accomplished, technology-driven Lead ... Pipeline Engineering: Design distributed processing frameworks, control flows, and configuration ...

Remote-USA Employment Type: Contract Department: Engineering Role Overview Koantek is seeking ... Experience with Databricks Apps and the broader Databricks platform is highly desirable but not ...

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

Lead Engineer, Data Platforms

Tempe, AZ · On-site +1

$111K - $133K/yr

Location Requirement: This position is eligible for remote work within any state Dutch Bros ... Successfully develop and deploy production-grade data solutions on Snowflake, databricks or similar ...

Data Engineer

Phoenix, AZ · On-site +1

$113K - $136K/yr

... DevOps, and Dynamics 365 data integration initiatives. The Exponential Technology Group (XTG) is a ... This opportunity is remote with the ideal candidate being located in DFW, Phoenix, Atlanta or ...

... remote work. As a Data Analyst, team members will be responsible for evaluating and improving U ... Engineering tables and jobs using Spark SQL and Python to meet analysis and reporting needs

Remote Databricks Developer information

What is a remote Databricks developer?

A Remote Databricks Developer is a software professional who specializes in building, managing, and optimizing data pipelines and analytics workflows on the Databricks platform, while working from a remote location. They use Databricks, which is based on Apache Spark, to process large datasets, develop ETL processes, implement machine learning models, and collaborate with data teams. Their responsibilities often include writing code in languages like Python, Scala, or SQL, integrating with cloud services, and ensuring data quality and security. Working remotely, they communicate with teams online and use cloud-based tools to complete their tasks efficiently.

How does a remote Databricks developer typically collaborate with cross-functional teams while working from different locations?

Remote Databricks Developers often work closely with data engineers, data scientists, and business analysts through virtual collaboration tools like Slack, Jira, and Zoom. Since team members may be distributed across various time zones, clear communication, regular stand-up meetings, and thorough documentation are essential for ensuring alignment on project goals and deadlines. Developers are also expected to participate in code reviews and shared knowledge sessions to maintain coding standards and support a collaborative environment. This structure helps ensure that complex data solutions are delivered efficiently and meet business requirements.

What are the key skills and qualifications needed to thrive as a remote Databricks developer, and why are they important?

To thrive as a Remote Databricks Developer, you need strong expertise in data engineering, programming languages like Python or Scala, and experience with big data frameworks, typically supported by a degree in computer science or a related field. Proficiency with Databricks platform, Apache Spark, cloud services (such as AWS or Azure), and relevant certifications like Databricks Certified Associate Developer are commonly required. Strong problem-solving, communication, and self-motivation are crucial soft skills for remote collaboration and project delivery. These skills and qualities ensure efficient development, scalable data solutions, and effective teamwork in distributed environments.

What is the difference between Remote Databricks Developer vs Data Engineer?

AspectRemote Databricks DeveloperData Engineer
Required SkillsProficiency in Databricks, Spark, Python, SQLProficiency in data pipelines, ETL, cloud platforms, SQL
Work EnvironmentCollaborates on data projects using Databricks platformBuilds and maintains data infrastructure across cloud environments
CertificationsDatabricks certifications often preferredCloud certifications (AWS, Azure), data engineering certifications

While both roles involve working with data and cloud platforms, a Remote Databricks Developer specializes in developing solutions within the Databricks environment, focusing on Spark and data analytics. A Data Engineer has a broader scope, designing and maintaining data pipelines and infrastructure across various platforms. The roles overlap in skills like SQL and cloud knowledge, but their primary focus and tools differ.

What are the most commonly searched types of Databricks Developer jobs in Chandler, AZ?

The most popular types of Databricks Developer jobs in Chandler, AZ are:

What job categories do people searching Remote Databricks Developer jobs in Chandler, AZ look for?

The top searched job categories for Remote Databricks Developer jobs in Chandler, AZ are:

What cities near Chandler, AZ are hiring for Remote Databricks Developer jobs?

Cities near Chandler, AZ with the most Remote Databricks Developer job openings:

Infographic showing various Remote Databricks Developer job openings in Chandler, AZ as of August 2026, with employment types broken down into 66% Full Time, 28% Contract, and 6% Nights. Highlights an 100% Remote job distribution.

Databricks Platform Architect

Koantek

Chandler, AZ • Remote

Contractor

Re-posted 9 days ago


Job description

Job Title: Lead Data Platform Architect / Data bricks Migration Lead Location: Remote Position Type: Contract Job Overview We are seeking an accomplished, technology-driven Lead Data Platform Architect / Migration Specialist to spearhead the modernization of our core enterprise financial and tax allocation engines. In this role, you will lead the architectural design, definition of migration strategies, and hands-on implementation to transition large-scale legacy relational database systems (SQL Server/T-SQL) into a modern, cloud-native Databricks Lakehouse platform. The ideal candidate will have extensive experience in high-throughput distributed systems, Databricks compute optimization, performance tuning, and complex pipeline orchestration.

Key Responsibilities Architecture & Strategy: Validate, refine, and own the target architecture on Databricks. Define robust migration strategies and production-ready reference patterns to convert 150+ complex stored procedures into PySpark and Structured/Declarative Pipelines (SDP). Pipeline Engineering: Design distributed processing frameworks, control flows, and configuration-driven parameter handling for both full and incremental recalculation modes.

Performance Optimization: Address performance deltas between small and large workloads. Architect and implement acceleration techniques such as caching, partition pruning, cluster sizing, and offline/pre-calculation strategies to maintain sub-30-second user-facing reporting SLAs. Orchestration & Observability: Design and deploy enterprise-level pipeline orchestration using tools like Apache Airflow or Databricks Workflows.

Integrate robust logging, error handling, and observability patterns into existing enterprise monitoring frameworks. Governance & Security: Implement data governance models, data lineage, and schema evolution utilizing tools like Unity Catalog. AI-Assisted Delivery & Code Quality: Establish best practices for AI-assisted code generation (e.g., using Claude or advanced LLMs), providing code-review patterns and refactoring frameworks to ensure maintainable and performant output

Team Enablement: Lead code walkthroughs, design reviews, and pair-programming sessions with the development team to accelerate knowledge transfer and technical excellence. Required Technical Skills & Qualifications Core Big Data Platform: Deep expert-level knowledge of Databricks (Lakehouse architecture, Delta Lake, Unity Catalog) and Apache Spark / PySpark. Legacy Database Expertise: Strong background in relational databases, with advanced proficiency in SQL Server, T-SQL, and Stored Procedures.

Ability to reverse-engineer and refactor legacy database logic into distributed paradigms. Orchestration Tools: Hands-on experience with Apache Airflow or similar modern workflow orchestrators. Performance Tuning: Proven track record in cost optimization (FinOps), cluster tuning, autoscaling configurations, and handling skewed data profiles.

CI/CD & DevOps: Experience with Infrastructure as Code (Terraform), data build tool (dbt), testing frameworks (PyTest), and automated Git-based workflows. Experience Level: 10+ years of experience in Data Engineering/Architecture, with at least 3+ years specifically leading large-scale cloud data migrations. Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

Preferred Certifications Databricks Certified Data Engineer Associate / Professional Databricks Certified Solutions Architect AWS Certified Database Specialist or equivalent Cloud Certifications