Senior Data Engineer (Strong Fabric)_Insurance project

Company: Techbank Software

Position: Senior Data Engineer (Strong Fabric)

Contract duration: 6 months (possibly extend)

Working time: Mon to Fri. 3pm - 11pm VNT

Location: Remote working, based in Viet Nam

Employment type: Full-time, open for 2nd job candidates

End Client: EU

Domain: Insurance

English requirement: Fluent, working directly with the client-side engineering team

Interview process: 4 rounds

• 2 rounds Techbank (HR round + Technical round)

• 2 round Client (English language verification test + Technical verification with Poland team)

CLIENT DESCRIPTION

The client operates in the international insurance sector, providing a broad range of products for both individual and business customers. The organization is undergoing a technology transformation focused on building a modern Microsoft Fabric-based data platform and modernizing existing integration and ETL capabilities within a cloud environment.

TECHNICAL REQUIREMENTS

Must Have

• Minimum 7 years of experience as a Data Engineer.

• Strong Microsoft Fabric expertise.

• Experience building and maintaining ETL/ELT solutions on Azure data platforms.

• Strong SQL and data transformation skills.

• Experience with Microsoft Azure (Data Lake, Azure SQL, Storage, Functions, and related data services).

• Knowledge of Microsoft Fabric services, particularly Data Factory, Lakehouse, and Data Pipelines.

• Experience integrating data via REST APIs, file-based integrations, and enterprise systems.

• Hands-on experience with data ingestion, transformation, validation, and enrichment processes.

• Experience with CI/CD and DevOps practices.

• Ability to design scalable and maintainable data solutions.

• Ability to define standards, architectural patterns, and engineering best practices.

• Experience creating and maintaining technical documentation.

• English proficiency at a minimum B2+ level.

Nice to have

• Insurance or financial services industry experience.

• Experience with Azure Functions.

• Knowledge of event-driven architectures.

• Experience with Python, Spark, or PySpark.

• Experience with AI-assisted software development.

• Exposure to Build-Operate-Transfer (BOT) delivery models.

RESPONSIBILITIES

• Design, build, and maintain ETL/ELT processes within Microsoft Fabric.

• Develop scalable ingestion frameworks supporting XML, JSON, CSV, API, and SFTP-based data sources.

• Implement data transformation, validation, and enrichment processes.

• Build reusable integration patterns supporting new partner onboarding.

• Integrate with the Nexus API Platform and other enterprise services.

• Develop API-based data enrichment processes.

• Implement routing logic to downstream systems, including Merlin and Helios.

• Ensure accurate handling of multiple insurance transaction types, including quotes, policies, claims, endorsements, and payments.

• Analyze existing integrations and processes operating on the DITTO platform.

• Migrate legacy ETL workloads into Microsoft Fabric.

• Simplify existing solutions and reduce technical complexity where possible.

• Collaborate with architects and client teams to define standards, best practices, and platform direction.

• Apply CI/CD and Infrastructure-as-Code practices where appropriate.

• Develop automated testing and data validation processes.

• Monitor and optimize pipeline performance, reliability, and cost efficiency.

• Support operational activities during the BOT Operate phase.

• Produce technical documentation, runbooks, and operational procedures.

• Support knowledge transfer activities to client teams.

• Contribute to reusable templates, accelerators, and onboarding frameworks.

• Leverage AI-assisted engineering tools while maintaining engineering quality standards.

ADDITIONAL IMPORTANT INFORMATION

• Project delivered in an international environment.

• First Microsoft Fabric implementation within the organization.

• Opportunity to help establish architectural standards, engineering practices, and operating models for the new data platform.

• Involvement in legacy platform migration and modernization initiatives.

• Close collaboration with Data Engineers, Architects, Integration Analysts, and business stakeholders.

• Environment supporting AI-assisted software development and modern data engineering practices.