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# Data Pipeline & Analytics Engineer

- **Company:** Jobs for Humanity
- **Location:** Phoenix, AZ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Query Performance, Airflow, Amazon Web Services, Business Analytics Applications, Data Analysis, Microsoft Azure, Cloud Database, Data Governance, Extract Transform Load (ETL), SQL Databases, Workflow Management Systems, Apache Spark, Data Management, Data Pipelines
- **Published:** August 31, 2026
- **Apply:** https://www.careerjet.com/jobad/us11a1c6ad229b9b89c9b7a6ba35251a81

## About the Role

* SQL (advanced)
 * Data modeling
 * ETL/ELT (Airflow, dbt or similar)
 * Workflow orchestration and monitoring
 * Cloud data stacks (AWS/GCP/Azure)
 * Big data tools (Spark)
 * Experience improving query performance and reducing processing costs
 * Experience implementing automated data quality monitoring
 * Proficiency building analytics-ready models and dashboards
 * Data governance / data stewardship experience
 * Strong analytical and problem-solving skills
 * Excellent communication with technical and non-technical stakeholders
 * High attention to detail and strong data quality mindset
 * Demonstrated ownership and continuous improvement

## Description

Join our team to build and evolve data pipelines and analytics solutions that transform raw data into reliable, governed insights stakeholders can trust and act on. Job Purpose
Design, build, and optimize scalable ETL/ELT and analytics-ready data models that improve performance, ensure high data quality through automated monitoring, and enable data-driven decision-making with accessible dashboards and governed datasets. Job Duties and Responsibilities

 * ETL/ELT (Airflow, dbt or similar)
 * Data pipeline design & optimization
 * Workflow orchestration and monitoring
 * SQL (advanced)
 * Data modeling
 * Big data tools (Spark)
 * Cloud data stacks (AWS/GCP/Azure)
 * Automated data quality monitoring
 * Query performance tuning
 * Analytics-ready models and dashboards
 * Data governance and governed datasets
 * Problem-solving and analytical thinking
 * Ownership and continuous improvement
 * Communication with technical and non-technical teams
 * Attention to detail and data quality mindset
 * Stakeholder management


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