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Remote Data Engineering Jobs in Phoenix, AZ (NOW HIRING)

Koantek

Databricks Platform Architect

Chandler, AZ · Remote

Lead Data Platform Architect / Data bricks Migration Lead Location: Remote Position Type: Contract ... Pipeline Engineering: Design distributed processing frameworks, control flows, and configuration ...

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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All JobsData Engineering JobsRemote Data Engineering JobsRemote Data Engineering Jobs in Phoenix, AZ

Remote Data Engineering information

See Phoenix, AZ salary details

$44.2K

$128.8K

$176.2K

How much do remote data engineering jobs pay per year?

As of Aug 8, 2026, the average yearly pay for remote data engineering in Phoenix, AZ is $128,797.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,700.00 and $136,500.00 per year, depending on experience, location, and employer.

How do remote data engineers typically collaborate with other team members across different time zones?

Remote data engineers often work with distributed teams, which requires strong communication and organization skills. They collaborate using tools like Slack, Zoom, and project management platforms to stay aligned on data pipeline development, troubleshooting, and deployment. Regular stand-ups, asynchronous documentation, and clear communication of progress are essential for ensuring everyone is on the same page, regardless of location. Flexibility in working hours and proactive scheduling of meetings help facilitate effective collaboration and project delivery.

What is remote data engineering?

Remote data engineering involves designing, building, and maintaining data systems and pipelines while working from a location outside of a traditional office. Remote data engineers use tools to collect, process, and store large sets of data, making it accessible for analysis and business decision-making. They collaborate with teams virtually, often using cloud-based technologies, to ensure that data infrastructure is reliable, scalable, and secure. This role requires strong technical skills in programming, databases, and data architecture, as well as the ability to communicate effectively in a distributed work environment.

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

To thrive as a Remote Data Engineer, you need strong programming skills (such as Python, Java, or Scala), experience with data modeling, ETL processes, and a solid understanding of database systems, often supported by a degree in computer science or a related field. Proficiency with big data tools like Apache Spark, Hadoop, cloud platforms (AWS, Azure, GCP), and certifications in these technologies is highly valued. Excellent problem-solving abilities, self-motivation, and clear communication are crucial soft skills for remote collaboration and project delivery. These competencies ensure effective data pipeline development, reliable data management, and seamless teamwork across distributed environments.

Can you work remotely as a data engineer?

Yes, remote data engineering roles are common, allowing professionals to work from various locations. These jobs often require strong skills in SQL, cloud platforms, and data pipeline tools, and may involve collaboration through online communication tools.

What is the difference between Remote Data Engineering vs Remote Data Analyst?

AspectRemote Data EngineeringRemote Data Analyst
Required CredentialsBachelor's in CS, Data Science, or related field; experience with SQL, Python, cloud platformsBachelor's in Statistics, Data Science, or related; proficiency in Excel, SQL, visualization tools
Work EnvironmentBuilds data pipelines, manages databases, works with cloud infrastructureAnalyzes data sets, creates reports, visualizes data insights
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, finance, retail, consulting

Remote Data Engineering focuses on designing and maintaining data infrastructure, while Remote Data Analysts interpret data to provide insights. Both roles require strong analytical skills but differ in technical depth and responsibilities.

What are the most commonly searched types of Data Engineering jobs in Phoenix, AZ? The most popular types of Data Engineering jobs in Phoenix, AZ are:
What are popular job titles related to Remote Data Engineering jobs in Phoenix, AZ? For Remote Data Engineering jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Remote Data Engineering jobs in Phoenix, AZ look for? The top searched job categories for Remote Data Engineering jobs in Phoenix, AZ are:
What cities near Phoenix, AZ are hiring for Remote Data Engineering jobs? Cities near Phoenix, AZ with the most Remote Data Engineering job openings:
Remote Data Engineering jobs near you
Infographic showing various Remote Data Engineering job openings in Phoenix, AZ as of August 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 100% Remote job distribution, with an average salary of $128,797 per year, or $61.9 per hour.

Databricks Platform Architect

Koantek

Chandler, AZ • Remote

Contractor

Re-posted 25 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

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