Top Senior Data Engineer Jobs in Phoenix, AZ

As a Senior Data Engineer, you'll design and maintain scalable data pipelines, optimize workflows with dbt and Snowflake, and mentor teams across functions to enhance data reliability and performance.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Lead and mentor a distributed data engineering team while architecting and building a next-generation, largely open-source data platform. Own hands-on POCs, pipeline design, schema and query optimization, ETL reliability, observability, testing, and CI/CD. Evaluate and recommend technologies, drive migration from vendor lock-in, and translate advertising and market research needs into robust technical solutions.

Lead design and ownership of a modern data platform: ingestion, Snowflake governance, pipeline reliability, migrations, data contracts, observability, and AI-enabled tooling. Partner with Analytics, Engineering, and vendors to deliver trustworthy data products and operate production pipelines.

Own and operate Databricks production pipelines for Sugar Predict: monitor, alert, incident response, SLA reporting, performance and cost optimizations, migrate legacy ETL, onboard tenants, enforce Delta Lake architecture and multi-tenant isolation, apply security and governance, enable observability, maintain runbooks, and support CI/CD and on-call rotations.

Own day-to-day Snowflake administration and the Sigma semantic layer; manage ingestion (Fivetran), dbt modeling scope, reverse-ETL to Salesforce/HubSpot/NetSuite, data quality SLAs, reporting and dashboards, partner with managed data vendors, design automation and reproducible month-end financial analytics, and drive analysis and experiments for AdOps, Sales, and Finance.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

Senior Data Engineer responsible for designing and implementing data solutions, ensuring data quality, and optimizing data systems.

Design, build, and deploy an ETL-based transactional data system to read and manage information state and transmit data via web services. Implement solution from provided design, perform unit testing, deploy to development/test, and collaborate with QA and DPS to resolve defects.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

The role involves building and optimizing data pipelines, modeling datasets, applying machine learning techniques, and supporting data strategies. Candidates should have experience with Python, data lakes, warehousing, and related technologies.

Design, build, and maintain scalable data pipelines, data models, ETL/ELT, and BI solutions. Collaborate with data scientists and stakeholders to improve data quality, accessibility, and PHI security. Develop and document workflows, optimize processing/storage, support data warehouse and visualization, and adopt AI tools to drive data-informed outcomes.

The Senior Data Analyst will manage the lifecycle of data analysis projects for RevolutionParts, develop complex models using SQL and Python/R, ensure data integrity, create dashboards in Looker, and mentor the team on data strategy.

The Senior Data Engineer II will build and optimize data solutions on Axon's Corporate Data Platform, design data pipelines, mentor team members, and uphold data governance and quality standards.

Design, build, and maintain dbt data models and scalable ELT pipelines; optimize performance-aware SQL on Snowflake; ensure observability, testing, and CI/CD; partner with product and engineering teams to meet analytic needs, mentor engineers, document business logic, and troubleshoot ingestion, transformation, and warehouse issues.

The Senior Data Engineer will construct and optimize data pipelines, work with cross-functional teams, ensure data quality and governance, and mentor junior engineers while leading advanced analytics initiatives.

Design, build, operate, and optimize large-scale data pipelines and foundational data products for Komodo's Healthcare Map. Improve pipeline reliability, observability, and performance; transform claims and EHR datasets; implement data quality, lineage, and monitoring; debug production workflows; collaborate with product and engineering teams; and enable downstream analytics and AI/ML use cases.

Technical lead on the Distribution Center data team building and scaling ETLs and data integrations using PySpark and DBT. Design robust pipelines to process large customer datasets, improve onboarding tooling, mentor engineers, manage contractors, and collaborate cross-functionally to support ML-driven grocery solutions.

Design and build cloud-based data applications and pipelines to analyze clinical data, enrich and provision datasets, and support clinical/operational processes. Mentor junior engineers, drive R&D for repeatable templates, collaborate with Product/Platform/Architecture, and prioritize source control, documentation, and simple solutions using modern data, ETL, and LLM tooling.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.