Data Engineer

Role Summary

We are seeking a Lead Data Engineer to spearhead the architecture, development, and maintenance of our modern data lake. In this role, you will be the bridge between raw fintech data and our Business Intelligence teams, ensuring that data is processed efficiently, securely, and accurately. You will champion our modern tech stack—heavily utilizing Python, Polars, and DuckDB—while deploying highly secure, containerized workloads within the Azure ecosystem.

• Data Lake Architecture & Governance: Design and implement a scalable, governed data lake solution tailored for analytical scaling.
• Pipeline Development: Build, orchestrate, and optimize high-performance data processing pipelines using Python, Polars, DuckDB, and SQL.
• Containerization & Deployment: Implement and manage Docker/containers for running data jobs reliably using Azure Containers.
• Security & Access Management: Drive secure development practices. Create and manage Azure roles and permissions (RBAC) to ensure strict data governance and secure environments.
• Cross-Functional Collaboration: Partner closely with the BI team to understand their reporting requirements (Power BI) and deliver the data models they need.
• CI/CD Implementation: Establish and maintain automated deployment pipelines using Azure Dev Ops.

• Data Engineering Expertise: Proven track record in core Data Engineering principles, data modeling, and data warehouse/data lake architecture.
• Core Programming: Exceptional proficiency in Python
  , SQL
  , Polars
  , and DuckDB
  .
• Cloud Infrastructure: Deep familiarity with the Azure Environment
  , including deploying and managing Azure Containers.
• Dev Ops & Automation: Strong practical knowledge of CI/CD pipelines (Azure Dev Ops) and Docker/containerization.
• Security Acumen: Solid understanding of cloud security, specifically in configuring Azure roles, identities, and access management.
• Domain Knowledge: Previous experience working with Fintech industry data
  , understanding its unique security and analytical requirements.
• Communication: Excellent communication skills with the ability to translate technical concepts to BI teams and business stakeholders.
• Team Leadership: Should have experience managing a team of 4-5 Data Engineers
• Collaboration: Strong capabilities of collaborating with Senior Stakeholder, Product Owner and team
• Consultative Mindset: Capabilities to provide an optimized solution and have Fin Tech and Wealth Management domain understanding
• Overall
  
  Experience:
  
  Overall, 8-10 Years of experience with minimum of 5-6 years of experience working on Data lake solutions

• Familiarity with Microsoft Dynamics
  , Salesforce
  , or similar CRM platforms.
• Knowledge of Azure Data Factory (ADF) for data integration.
• Experience with Microsoft Purview for unified data governance.
• BI & Analytics: Power BI

Internal round followed by client round’s.