Data Engineer – Power BI & Azure Data Pipelines

UNC; Chapel Hill (NC); On-site; USD 60,000 - 90,000; Part time

Job summary

Talentify is seeking a temporary data engineering professional in Chapel Hill to support data integration, modeling, and BI initiatives. The role emphasizes building reliable data pipelines and Power BI solutions on SQL Server and Azure-based platforms such as Microsoft Fabric or Azure Databricks.

The candidate will develop and optimize SQL queries, validate data quality, and assist with ETL processes across multiple source systems, contributing to curated data layers and semantic models for

Qualifications

• Experience building and optimizing SQL Server queries, views, and procedures.
• Experience with data validation and quality checks across data layers.
• Experience with ETL tools (Informatica IICS or SSIS).

Responsibilities

• Develop, maintain, and optimize SQL Server queries, views, functions, and stored procedures.
• Perform data validation, reconciliation, and quality checks across source, transformation, and curated data layers.
• Assist with ETL processes using Informatica IICS, SSIS, or comparable tools.
• Support integration of data from multiple sources into SQL Server environments.
• Develop, maintain, and support Power BI reports, dashboards, and data models.
• Work with lakehouse architecture patterns to support reporting datasets.
• Prepare and maintain documentation for data pipelines and mappings.

Skills

SQL Server

Power BI

Data modeling

ETL development

Azure Databricks

Microsoft Fabric

DAX/Power Query

Tools

Informatica IICS

SSIS

Azure data platforms

Job description

Talentify is seeking a temporary data engineering professional in Chapel Hill to support data integration, modeling, and BI initiatives. The role emphasizes building reliable data pipelines and Power BI solutions on SQL Server and Azure-based platforms such as Microsoft Fabric or Azure Databricks.

The candidate will develop and optimize SQL queries, validate data quality, and assist with ETL processes across multiple source systems, contributing to curated data layers and semantic models for