Data Engineer - Fabric Pipelines, BI & AI (Hybrid)

MissionStaff; Philadelphia (Philadelphia County); Hybrid; USD 83,000 - 124,000; Full time

Job summary

MissionStaff is seeking a data engineer for a hands-on contract role to develop and expand a Microsoft Fabric data platform in the Philadelphia area. You will build data pipelines, modernize data flows, and create scalable data products to support analytics, Power BI, and AI-driven solutions.

Join a high-impact initiative and collaborate with engineering teams to productionize pipelines, apply data quality processes, and share knowledge through code reviews and mentorship.

Qualifications

• Advanced Python and SQL skills.
• Strong hands-on experience with Microsoft Fabric.
• Experience with Fabric Lakehouse, Notebooks, Data Factory, and Pipelines.
• Knowledge of medallion architecture (Bronze/Silver/Gold).
• Experience implementing data quality, cleansing, and validation processes.
• Experience integrating APIs, relational databases, files, and enterprise data sources.
• Ability to design reliable, scalable data pipelines.
• Experience developing complex transformations including historization and business calculations.
• Ability to communicate technical decisions clearly to stakeholders.
• Strong collaboration and knowledge-transfer skills.

Responsibilities

• Build and productionize data pipelines on a Microsoft Fabric platform.
• Modernize existing data flows and develop scalable data products.
• Support analytics, Power BI, and AI-driven solutions in production.

Skills

Python

SQL

Microsoft Fabric

Data Pipelines

Power BI

APIs

Data quality

Tools

Git

CI/CD

Job description

MissionStaff is seeking a data engineer for a hands-on contract role to develop and expand a Microsoft Fabric data platform in the Philadelphia area. You will build data pipelines, modernize data flows, and create scalable data products to support analytics, Power BI, and AI-driven solutions.

Join a high-impact initiative and collaborate with engineering teams to productionize pipelines, apply data quality processes, and share knowledge through code reviews and mentorship.