Top Data Engineer Jobs in Phoenix, AZ

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design, build, and optimize BigQuery-based data solutions and data models. Manage data storage, partitioning, clustering, ETL, and data quality. Collaborate with architects, data scientists, and engineers to deliver scalable, secure cloud data infrastructure and documentation; senior hires provide technical leadership, code review, and process improvement.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

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.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

The Principal Data Engineer will provide technical leadership, design complex software architectures, mentor teams, and drive data engineering best practices within the organization.

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).

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.