Data Engineer

Description

We are looking for a Data Engineer to strengthen and expand an established Microsoft Fabric data environment. This Long-term Contract position is ideal for someone who can turn business data into reliable, well-structured assets that support reporting and decision-making. The role requires a hands-on engineer who can shape data architecture, build scalable pipelines, and communicate clearly with both technical teams and business stakeholders.

Responsibilities:

Expand and improve an existing Microsoft Fabric platform to support dependable, scalable analytics solutions.

Create and maintain a layered data architecture across Bronze, Silver, and Gold tiers, with emphasis on delivering trusted and business-ready curated datasets.

Build ingestion and transformation processes for Salesforce data along with information from additional enterprise sources.

Develop data models that improve accuracy, usability, and reporting value by evaluating structure, relationships, and downstream needs.

Support the shift away from older warehouse and spreadsheet-driven reporting practices by introducing more modern data engineering approaches.

Work autonomously to manage priorities while providing regular updates on progress, technical decisions, and potential risks.

Collaborate with business partners to understand reporting goals and translate them into practical data solutions.

Contribute to data processing and integration workflows using technologies such as Python, Spark, ETL frameworks, and related platform tools.

Requirements

Demonstrated hands-on experience building solutions within Microsoft Fabric.
Strong knowledge of data modeling principles and layered data design, including Bronze, Silver, and Gold architecture.
Experience working in Azure-based data ecosystems and cloud data platforms.
Proven ability to evaluate requirements critically and recommend sound data design choices rather than simply executing requests.
Background in integrating, transforming, and modeling Salesforce data for analytics use cases.
Familiarity with traditional warehousing or spreadsheet-heavy reporting environments and how to modernize them.
Exposure to Power BI and an understanding of how reporting needs influence data structure and delivery.
Proficiency with core data engineering tools and concepts, including Python, Apache Spark, ETL processes, and related big data technologies.

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