About the role

Role Overview

As a Data Engineer, you will be the backbone of our data-driven initiatives. You will design, build, and maintain robust data pipelines and cloud infrastructure on Azure to power advanced analytics and Machine Learning models for leading global banks and fintech firms. This is not just a "behind-the-scenes" role; you will collaborate with senior architects and business stakeholders to solve complex financial data challenges while ensuring the highest standards of security, quality, and scalability.

Key Responsibilities

Pipeline Development: Design and implement end-to-end ETL/ELT workflows using Azure Data Factory (ADF) and Azure Functions.
Data Architecture: Build and manage scalable data warehouses and lakes (SQL Server, Blob Storage) using industry-standard modeling techniques.
Big Data Processing: Write high-performance data processing scripts using Python and PySpark to handle large-scale financial datasets.
Cloud Governance: Manage Azure resources via the Portal, implementing RBAC, security protocols, and governance policies to protect sensitive financial data.
DevOps & Deployment: Own the deployment lifecycle using Azure DevOps, CI/CD pipelines, and Docker to ensure seamless production releases.
Quality Assurance: Monitor data health and implement validation frameworks to ensure data integrity for downstream ML models.
Stakeholder Engagement: Act as a technical consultant for clients, translating business requirements into scalable technical solutions.

Required Skills & Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
Domain Expertise: 2–5 years of experience in Data Engineering, ideally within Banking, Insurance, or Fintech.
Technical Stack:
- Orchestration: Hands-on expertise in Azure Data Factory (ADF).
- Compute: Proficiency in Python, PySpark, and advanced SQL.
- Storage: Experience with Azure SQL, Synapse, and Blob Storage.
- Security: Deep understanding of Azure Key Vault, RBAC, and networking fundamentals.
Tools: Experience with Git, Azure DevOps, and containerization (Docker).
Soft Skills: Strong analytical mindset, attention to detail, and the ability to communicate technical concepts to non-technical audiences.