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Data Pipeline & Analytics Engineer
Job in
Avondale, Maricopa County, Arizona, 85323, USA
Listed on 2026-09-04
Listing for:
Jobs for Humanity
Full Time
position Listed on 2026-09-04
Job specializations:
-
IT/Tech
Data Engineering, Data Analyst, Data Warehousing
Job Description & How to Apply Below
Jobs for Humanity is partnering with Elegax to build an inclusive and just employment ecosystem. Therefore, we prioritize individuals coming from all walks of life.
Company Name:
Elegax
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 PurposeDesign, 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
Required Qualifications
- 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
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