Data Engineer

Hirerising; Phoenix (AZ); On-site; USD 110,000 - 160,000; Full time

Job summary

Hirerising in Phoenix, AZ is seeking a Data Engineer with 5–7 years of experience in data integration, ETL/ELT development, reverse ETL, and SQL. The ideal candidate will have hands‑on experience with AWS data services, data lakes, data warehouses, and data connectors.

The successful candidate will help develop and maintain reliable data pipelines, integrate data from multiple sources, and support reporting and analytics requirements.

Qualifications

• 5–7 years of experience in data engineering and ETL development.
• Hands-on experience with ETL/ELT, reverse ETL, and data integration/connectors.
• Experience with AWS data services, including Redshift, RDS, and Glue.
• Hands-on with data lakes and data warehousing.

Responsibilities

• Develop, maintain, and troubleshoot ETL/ELT and reverse ETL pipelines.
• Configure and maintain data connectors using Fivetran or similar tools.
• Work with AWS services including Redshift, RDS, and Glue.
• Build and maintain data lake and data warehouse solutions.
• Write and optimize SQL queries for data extraction, transformation, and reporting.
• Integrate data from multiple systems and ensure accurate delivery.
• Monitor pipeline performance, resolve data issues, and maintain quality.
• Collaborate with analysts and stakeholders to support data requirements.
• Document data workflows and integration processes.
• Follow best practices for data security, reliability, and maintainability.

Skills

Data engineering

ETL development

SQL

AWS

Data integration

Tools

Fivetran

Amazon Redshift

AWS Glue

RDS

Job description

Important: This is an in-office position in Phoenix, Arizona. Local Phoenix-area candidates are strongly preferred. Visa sponsorship is not available for this position. (NO C2C please)

Job Summary

We are looking for a Data Engineer with 5–7 years of experience in data integration, ETL/ELT development, reverse ETL, and SQL. The ideal candidate will have hands‑on experience working with AWS data services, data lakes, data warehouses, and data connectors.

The successful candidate will help develop and maintain reliable data pipelines, integrate data from multiple sources, and support reporting and analytics requirements.

Key Responsibilities

• Develop, maintain, and troubleshoot ETL/ELT and reverse ETL pipelines.
• Configure and maintain data connectors using Fivetran or similar data integration tools.
• Work with AWS services, including Amazon Redshift, Amazon RDS, and AWS Glue.
• Build and maintain data lake and data warehouse solutions.
• Write and optimize SQL queries for data extraction, transformation, validation, and reporting.
• Integrate data from multiple systems and ensure accurate, consistent data delivery.
• Monitor pipeline performance, resolve data processing issues, and maintain data quality.
• Collaborate with data analysts, business stakeholders, and other engineering teams to support data requirements.
• Document data workflows, transformations, and integration processes.
• Follow established best practices for data security, reliability, and maintainability.

Required Qualifications

• 5–7 years of experience in data engineering, ETL development, or a related role.
• Approximately 5 years of hands‑on experience with ETL/ELT, reverse ETL, and data integration/connectors.
• Experience working with AWS data services, including Amazon Redshift, Amazon RDS, and AWS Glue.
• Hands‑on experience with data lakes and data warehousing.
• Strong SQL skills, including joins, aggregations, data transformations, and query optimization.
• Experience developing and supporting data pipelines in production environments.
• Understanding of data modeling, data quality, and data integration concepts.
• Strong troubleshooting, analytical, and communication skills.

Preferred Qualifications

• Familiarity with AI tools such as Claude for coding, troubleshooting, or workflow automation.
• Experience with data orchestration and pipeline monitoring tools.
• Familiarity with cloud‑based data security and governance practices.