Top Remote Data Engineer Jobs in Phoenix, AZ

The Senior Data Engineer will create and manage data pipelines, ensuring data quality, collaborating with teams for data-driven decisions, and evolving the data platform.

The Senior Data Analytics Engineer analyzes and delivers business intelligence solutions, builds data pipelines, and collaborates with clients to meet their data-driven needs.

The Senior/Staff Data Engineer will design and scale the core data platform for healthcare systems, focusing on infrastructure, tooling, and governance while mentoring engineers and collaborating with various teams.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Build, operate, and modernize scalable data pipelines and warehouse environments for lottery reporting, analytics, and AI readiness. Improve ingestion, transformations, data models, contracts, observability, lineage, and onboarding patterns. Automate operations, troubleshoot production issues, mentor engineers, and support migration from legacy to cloud architectures while ensuring data quality, reliability, and regulatory continuity.

The Solution Engineer - Data Engineering Specialist will provide technical leadership in the financial services sector, collaborating with sales teams to demonstrate Snowflake's data platform capabilities while engaging with technical and executive stakeholders.

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

Design, build, and maintain backend systems and data interactions for healthcare reporting. Develop APIs, data models, ETL pipelines, and interfaces; optimize performance, ensure data integrity and security, and support automated testing and CI/CD. Collaborate in Agile teams and reduce technical debt while enabling integrations with frontend and third-party systems.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

Lead design, build, and maintain scalable batch and streaming data pipelines and data warehouse models. Ensure data quality, observability, performance, and security. Own workstreams, support production incidents, and enable analysts and downstream applications.

Administer, stabilize, and scale Oura's global Databricks environment. Manage workspace governance, cluster policies, access control, cost monitoring, job scheduling, and incident SLAs. Onboard teams, resolve performance and reliability issues, and partner with IT and Data Architecture. Over time contribute to pipeline development, Spark optimization, CI/CD, and infrastructure-as-code to support domain-oriented data mesh initiatives.

Lead the vision and execution for scalable data governance and quality across Airbnb. Architect future-proof data ecosystems, define best practices and tooling, drive cross-functional policy implementation (privacy, legal, infosec), mentor senior engineers, and influence executive strategy to embed stewardship into the data product lifecycle.

The Senior Data Engineer will design and manage data pipelines, work with complex datasets, and collaborate with teams to enhance data product integrity and effectiveness.

Design, build, and operate large-scale AWS-based ETL pipelines using Glue/PySpark to ingest, transform, and curate multi-format data into Apache Iceberg tables. Ensure data quality, metadata management, semantic layer creation (Trino/Athena), CloudFormation deployments, documentation, and compliance within a FedRAMP federal agile environment.

Serve as a data quality and QA engineer on a federal data platform: build automated AWS Glue/Deequ data quality checks, validate schemas and ETL pipelines, create and run unit/integration tests with 90% coverage, support IV&V and UAT, perform static/security scans, produce test documentation, monitor production ETL, and collaborate with data stewards and IV&V teams.

Define and lead the technical architecture for a lakehouse on AWS, build production pipelines and data models for time-series and multi-source data, introduce data governance and catalog foundations, author platform playbooks (ADRs, runbooks, Terraform modules), deliver projects end-to-end, and mentor engineers while representing the platform to senior leadership and non-technical stakeholders.

Design, build, and maintain scalable ETL/data pipelines across distributed systems. Ensure data quality, integrity, security, and high availability. Monitor and optimize pipeline performance, perform root cause analysis, document systems, collaborate with global teams, and participate in on-call rotations to support production data systems.

Lead design and delivery of a cloud-based data platform: implement CDC/streaming ingestion, modeling, transformations, cross-region DB replication, observability, governance (PII, lineage, access), and enable downstream marts while mentoring engineers and driving IaC/automation-first practices.

Design, build, deploy, and operate scalable production data pipelines and workflows (Airflow, BigQuery, dbt). Own medium-sized platform initiatives end-to-end, ensure SLA-driven freshness, quality, and cost controls, implement monitoring and CI/CD, participate in on-call incident response, optimize warehouse performance, and collaborate with analytics, data science, product, and engineering stakeholders.

Design, build, and maintain reliable data pipelines and models using SQL, Python, and Databricks/Spark. Translate multi-source schemas into scalable data models, enforce data hygiene and privacy, add tests/documentation/lineage, and collaborate with analytics, product, marketing, and vendor teams to deliver trustworthy data for personalization and marketing.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Design, build, and own data infrastructure and ETL/ELT pipelines (Airflow, S3, Lambda) into a governed Snowflake warehouse. Implement data quality, IaC (Terraform/CDK), CI/CD, and analytics solutions (SQL, Tableau). Enable ML/LLM use cases like forecasting and anomaly detection and deliver automated reporting and data-driven marketing outputs.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Build and maintain scalable ETL pipelines using Python, Spark, and Airflow; collaborate with AI/ML and Product teams to deliver AI-native data products; identify and resolve ETL bottlenecks; ensure code quality through reviews and tests; own sprint deliverables and contribute to roadmap planning and major epics.