Top Remote Data Engineer Jobs in Phoenix, AZ

Designs, develops, and supports analytics data solutions for clients, including Snowflake and dbt ingestion and transformation pipelines, data marts, validation, monitoring, governance, and documentation. Manages multiple consulting projects, budgets, timelines, quality assurance, troubleshooting, client communication, and implementation planning. Requires strong data engineering experience, regulated-industry knowledge, stakeholder communication, and willingness to travel.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, including Bronze, Silver, and Gold data layers. Build reliable streaming and batch pipelines with Kafka, Spark, and Airflow; manage Iceberg, Delta, and Hudi table formats; optimize Trino, Starburst, and Databricks queries; and improve platform reliability, data quality, and performance. Collaborate with data scientists, analysts, and product teams to deliver scalable data platform solutions.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Build and maintain scalable ETL/ELT pipelines and data foundations using Databricks, dbt, and AWS. Transform raw structured and unstructured data into reliable datasets for product analytics and data science, including telemetry, streaming data, and feature engineering. Develop dashboards in Tableau and Databricks, optimize workflows, and collaborate with Product, Engineering, analytics, and data science stakeholders to support product health, adoption, and performance insights.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Designs, builds, deploys, and maintains scalable data architectures, databases, ETL/ELT pipelines, API integrations, and cloud data solutions in AWS GovCloud. Migrates on-premises databases to cloud platforms, optimizes data models and performance, implements governance and security controls, and supports reporting and analytics. Collaborates with architects, data scientists, and stakeholders while documenting systems and conducting integration, validation, cost, and risk assessments.

Designs, builds, and maintains cloud data pipelines and ETL/ELT processes supporting VA healthcare analytics. Responsibilities include patient matching, record linkage, data harmonization, quality management, documentation, performance optimization, and pipeline support. Collaborates with analysts and stakeholders to develop scalable solutions and Power BI reports using Databricks SparkSQL. The role requires federal healthcare data experience, strong SQL or Spark SQL skills, and the ability to obtain a VA Public Trust clearance.

Entry-level data engineering development program involving three rotations, cloud-native data platforms, batch and real-time pipelines, distributed systems, ETL/ELT, data modeling, AI platforms, and enterprise data products. Participants use AWS, Spark, Java, Python, SQL, Snowflake, Kafka, and related technologies while gaining experience with LLM architectures, APIs, microservices, and secure production deployments.

Data Engineer Interns will gain hands-on experience building batch and real-time data pipelines using AWS, Spark, Java, Python, and SQL. They will learn data modeling, ETL/ELT, distributed systems, streaming technologies, and AI platforms, including LLM, RAG, vector database, and agentic AI architectures. Interns will collaborate with data engineers, contribute to secure data and AI solutions, and participate in mentorship and professional development activities.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

The Principal Data Engineer designs and builds scalable cloud-native data platforms, pipelines, frameworks, and governed data products supporting analytics, reporting, AI, and operational workloads. Responsibilities include Databricks, Spark, Delta Lake, Azure, SQL Server, batch and streaming processing, data integration, performance optimization, governance, modernization, troubleshooting, architecture reviews, and mentorship. The role provides senior technical leadership without direct people management and supports insurance-domain data across policy, claims, billing, finance, and regulatory functions.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Designs, develops, and supports SAP HANA reporting solutions and enterprise data products. Responsibilities include building data pipelines, calculation views, BI visualizations, embedded analytics, validation processes, metadata documentation, demos, and user training. The role partners with stakeholders, analysts, data scientists, and process teams to translate business requirements into deployable data solutions and improve the organization’s data platform.

Supports data engineering, ETL, database, platform, and integration workstreams. Responsibilities include data extraction, pipeline testing, environment setup, database support, source-system profiling, data cleansing, Ed-Fi ODS loading, validation, Oracle and SQL Server migration support, SharePoint configuration, year-end rollover tasks, and technical documentation under senior staff guidance.

Build and configure a government Databricks workspace, develop ingestion pipelines from enterprise systems, and prepare data for reporting. Implement Unity Catalog governance, metadata, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and operating procedures. The role also requires integrating legacy systems, optimizing Delta Lake pipelines, and supporting long-term platform sustainment.

Performs data engineering operations including SQL scripting, ETL and data validation, query optimization, data analysis, dashboard maintenance, task-result validation, and troubleshooting with engineering and DBA teams. Ensures data integrity and compliance, manages ticket-based assignments, and supports data production and implementations. The role may require after-hours or weekend support and includes preferred experience with network security platforms, certificate management, and automation scripting.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.