We are looking for a Data Engineer to lead the development, maintenance, and optimization of our enterprise data platform built on the Microsoft Fabric ecosystem. In this role, you will own end-to-end data pipelines, handling ingestion, transformation, data modelling, and delivery across all architecture layers.
You will integrate critical core systems into a regulated financial services environment. This is an impactful role ideal for a proactive engineer who can elevate data governance, optimize system performance, and architect reliable data solutions for business-wide analytics.
Key Responsibilities
- Pipeline Architecture & ELT: Design, build, and optimize robust data ingestion and transformation pipelines using Microsoft Fabric Data Factory Gen2 and custom ELT processes.
- System Integration: Integrate complex data from diverse sources including Oracle Flexcube, relational SQL databases, REST APIs, third-party platforms, and flat files.
- Data Modeling & Analytics: Architect and maintain scalable semantic layers and data models (e.g., Star Schema) for downstream reporting and analytics.
- Quality & Governance: Drive data governance and data quality frameworks, metadata management, pipeline monitoring, and validation rules.
- Platform Optimization & Incident Response: Proactively monitor platform performance, troubleshoot pipeline failures, lead root cause analyses, and implement preventive measures.
- Continuous Improvement: Establish best practices for CI/CD, technical documentation, and BI processes within a regulated framework.
Requirements
- Strong command of SQL, Data Warehousing, Data Modeling (Star Schema), and Python.
- Hands-on experience with Microsoft Fabric (Data Factory Gen2, OneLake), Power BI, Power Query (M Language), DAX, and Azure ecosystem components.
- Proficiency working with relational databases (Oracle, PostgreSQL, SQL Server), REST APIs, and version control tools (Git, Azure DevOps).
- Experience operating within financial services, banking systems (e.g., Oracle Flexcube), or regulated environments.
Benefits
Education and experience
- Bachelor’s degree in Computer Science, Information Systems, Data Analytics, or equivalent practical data engineering experience.
