Top Data Engineer Jobs in Phoenix, AZ

Leads payer healthcare data engineering and analytics initiatives, designing pipelines, data models, and interoperability frameworks for claims, membership, and provider data. Directs large technical programs, advises clients on data architecture and modernization, validates data quality and HIPAA controls, applies statistical and machine learning methods, coaches technical teams, and presents actionable recommendations to stakeholders.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Designs, develops, tests, and maintains data architectures, ETL pipelines, databases, and large-scale processing systems. Builds scalable data infrastructure, improves data delivery processes, and ensures data quality, integrity, availability, and performance. Collaborates with data scientists and architects while troubleshooting pipelines and systems. The role requires a technical bachelor’s degree, at least two years of data engineering experience, U.S. citizenship, and knowledge of SQL, data warehousing, and data integration.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Build and maintain batch and streaming data pipelines for high-volume IoT telemetry and operational data in Databricks. Design governed Bronze, Silver, and Gold data models, deploy pipelines with Asset Bundles, improve data quality and access controls, and support data science, machine learning, and agentic AI workloads. Partner with DevOps, Product, Data Science, and business stakeholders to mature the enterprise lakehouse platform.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain production data pipelines, dbt models, Airflow workflows, and governed datasets supporting People Analytics. Integrate Workday and other HR systems into reliable Snowflake data products, implement data quality monitoring, row-level security, semantic models, and AI agents. Partner with engineers, analysts, data scientists, and business stakeholders to deliver trusted data solutions, communicate technical trade-offs, enforce governance, and mentor other engineers.

Designs, develops, and supports scalable batch and streaming data pipelines, data products, models, API integrations, and data quality frameworks. The role enables analytics, reporting, AI/ML, and operational use cases using Snowflake, AWS, Python, Spark, and advanced SQL. Responsibilities include data governance, security, observability, automated testing, CI/CD, monitoring, troubleshooting, and optimization while collaborating with business, analytics, product, engineering, and data science teams.

The Data Engineer II supports customer workload onboarding through requirements gathering, query conversion, schema design, metadata analysis, data loading, workload testing, and performance analysis. The role collaborates with Solutions Architects and engineering teams to prepare pre-pilot workloads, create validation assets and technical documentation, assist with system sizing and timelines, and contribute to GDPR and data rolloff procedures.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Designs, develops, and maintains secure, scalable data pipelines, curated datasets, and data products supporting analytics and AI. Uses PySpark, SQL, Microsoft Fabric, Azure Synapse, dimensional modeling, and lakehouse architectures. Ensures data quality, governance, security, monitoring, performance, and production reliability while applying software engineering and DevOps practices. Partners with analysts, engineers, data scientists, and business stakeholders to translate requirements into trusted data solutions and improve platform efficiency.

Develop and lead scalable cloud data engineering solutions for data warehouse migration, modernization, analytics, and ETL/ELT projects. Design and implement solutions using Google Cloud technologies, SQL, Python, and BigQuery. Create testing strategies and plans, collaborate with customers and development teams, translate business requirements into technical solutions, and contribute to an agile environment.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark processes; supports Azure and Microsoft Fabric data platforms; monitors production reliability; collaborates with stakeholders; participates in code reviews and documentation; and mentors junior engineers.

Designs, develops, and maintains scalable data systems, pipelines, ETL processes, databases, and data warehouses supporting public health operations. Responsibilities include data integration, cleansing, transformation, governance, security, monitoring, optimization, and collaboration with analysts, data scientists, and stakeholders. The role also provides technical guidance, training, and mentorship while supporting sustainable data infrastructure and traveling domestically up to 10%.

Build and maintain scalable ETL/ELT pipelines, integrate data from APIs, databases, and third-party systems, and develop reliable data models for analytics and reporting. Ensure data quality, governance, security, and accessibility while supporting batch and real-time processing. Monitor pipeline performance, troubleshoot failures, and collaborate with Product, Analytics, Engineering, and Cloud teams to improve data architecture and enable data-driven decisions.

Designs, develops, and maintains scalable data pipelines, integrations, warehouses, data marts, and models. Ensures data quality, security, governance, and regulatory compliance while monitoring pipeline performance. Collaborates with engineering, product, analytics, and business stakeholders to deliver reporting and analytics solutions. The role also documents data architectures, supports master data management, evaluates technology improvements, and contributes to enterprise data strategy in a healthcare technology environment.

Build and operate scalable data pipelines, governed data models, semantic layers, and analytics products. Partner directly with business stakeholders to translate requirements into production-ready data and AI solutions. Develop SQL and Python applications, LLM and agentic workflows, retrieval systems, and automated testing. Ensure data quality, lineage, monitoring, reliability, cost efficiency, and compliance for PHI and genomic data. Collaborate with platform and AI teams on architecture, governance, CI/CD, and operational excellence.

Designs, builds, and operates scalable batch and streaming lakehouse pipelines using Databricks, Spark, PySpark, Delta Lake, and Unity Catalog. Responsibilities include data ingestion, transformation, governance, orchestration, performance optimization, CI/CD integration, monitoring, data quality, and documentation. The role supports analytics, machine learning, anomaly detection, fraud analytics, and risk scoring while collaborating with technical stakeholders and Agile teams. A Public Trust determination is required.

Leads a small data engineering team while contributing hands-on development. Responsibilities include designing and maintaining dbt and Python ETL pipelines, managing data quality and UAT, coordinating implementation timelines, conducting code reviews, mentoring engineers, documenting systems, and supporting project delivery. The role also involves building and deploying AI agents for data quality, pipeline monitoring, and analytics workflows, with a focus on healthcare claims data and large-scale datasets.

Develop and operate cloud-based data pipelines, dimensional data models, data warehouse solutions, CI/CD automation, and platform governance controls. The role integrates APIs and diverse data sources while supporting security, monitoring, incident response, and reliable data product delivery. Responsibilities include collaborating with product owners and engineering teams, participating in design and code reviews, troubleshooting technical issues, and applying repeatable DevOps practices.

Build and own AAG’s consolidated data warehouse, including production-grade infrastructure, ELT pipelines, dimensional models, and reporting layers. Integrate data from ERP, CRM, and manufacturing systems into Databricks on Azure, maintain reliable data flows, and support business decision-making across the organization. Collaborate with leadership to translate requirements into code, while managing system development, maintenance, documentation, and continuous improvement.

Leads end-to-end data engineering solutions, including pipeline design, data modeling, testing, deployment, optimization, production support, and data quality. Partners with stakeholders to develop cloud-based data platforms, prototypes, technical standards, and scalable analytic systems. Provides technical leadership, conducts design and code reviews, mentors team members, and ensures compliance with risk and data management policies.

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.