Top Remote Data Engineer Jobs in Phoenix, AZ

Build and maintain data pipelines, applications, workflows, and governance in Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot/MRO capacity, throughput, and bottlenecks, integrate ERP/MRO data into simulation models, deliver decision-support briefings, and support platform monitoring, automations, and client engagement.

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.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Design, build, and maintain AWS-based data pipelines and ETL processes using Glue, Spark/PySpark and EMR. Integrate multiple data sources into data lakes and Redshift, implement DevOps CI/CD practices, and work with Kafka, Hudi, and various AWS storage and analytics services.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design and build a medallion architecture using dbt and Microsoft Fabric, create and maintain data ingestion pipelines, and implement governance, lineage, documentation, and quality checks to support self-serve analytics while collaborating with cross-functional teams.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Own the 2-3 year architecture and reliability of high-volume catalog, pricing, and inventory ingestion. Lead migrations from legacy batch to modern systems, define data SLAs, build observability and orchestration, mentor senior engineers, and resolve cross-team, high-severity data problems.

Designs and implements scalable data and technology solutions, develops ETL pipelines and APIs, manages cloud and big-data platforms, maintains data models, supports monitoring and scheduling, participates in Agile development and technical reviews, and mentors junior engineers.

Seeking a Senior BODS Developer to develop and maintain complex BODS processes for data transformation. The role includes improving data quality using Information Steward and GAC/URAC techniques, collaborating with teams, creating documentation, and mentoring others.

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.

The Senior Data Engineer will design and implement complex data models, collaborate on data governance, lead data initiatives, and mentor team members.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

Build and manage data pipelines, ETL workflows, and DAGs using dbt, Apache Airflow, Python, SQL, and Snowflake. Develop custom Airflow operators and hooks, perform data modeling and source-to-target mapping, and implement data quality and cleansing processes. Use cloud platforms, CI/CD, version control, and Snowflake utilities including Snowpipe, Tasks, Streams, stored procedures, and data sharing. Collaborate across teams, communicate insights, solve complex problems, and participate in on-call escalation.

The Senior Data Engineer will design scalable data processing frameworks using Java and Apache Spark, manage data pipelines, and mentor junior engineers.

Lead design, build, and maintain scalable batch and real-time data pipelines, ETL/ELT workflows, data warehouses/lakehouses, and streaming solutions. Optimize performance, ensure data quality, security, observability, and CI/CD for production systems while mentoring engineers and collaborating with stakeholders for analytics and ML needs.

Lead data science and analytics initiatives for healthcare, overseeing model development, data integrity, and large-scale automated analyses. Provide actionable insights, mentor teams, collaborate with cross-functional partners, evaluate payment integrity campaigns, and present findings to stakeholders to optimize revenue, quality, consumer experience, and healthcare costs.

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

Design, build, and maintain data warehouse infrastructure, semantic/dimensional models, metrics layer, and BI dashboards (Sigma, Power BI, Databricks). Build secure ELT pipelines, enforce governance and row-level security (Entra ID), enable self-service analytics and AI/ML integrations, triage BI issues, train stakeholders, and collaborate in Agile to deliver reliable, performant reporting and curated data products.

Leads data engineering strategy, architecture, delivery, support, and lifecycle management for secure, scalable cloud and hosted data solutions. Collaborates with technical and business stakeholders to deliver complex datasets, establish enterprise data standards, manage data engineering teams, and oversee maintenance, change management, capacity planning, business continuity, disaster recovery, and regulatory security requirements. The role also evaluates third-party tools and supports Azure DevOps, SDLC, CI/CD, and technical innovation.