Data Engineer

Appealing Points:

• Build Modern Data & AI Solutions – Develop data pipelines using Microsoft Fabric and integrate enterprise data with AI platforms such as Copilot and Claude to enable AI-powered analytics.
• Drive Business Intelligence & Data Governance – Design Power BI dashboards, semantic models, and data solutions while ensuring high data quality, governance, and compliance.
• Collaborate on Global Data Initiatives – Partner with business, IT, and global data teams to deliver scalable analytics solutions in a fast-paced, cross-functional environment.

Annual salary: 8 million and above

Job Responsibilities:

• Data pipeline development: Design, build, and maintain ETL/ELT pipelines using Microsoft Fabric (Data Factory, Dataflows Gen2, Lakehouses) to ingest, transform, and serve data from internal and external sources
• Power BI analytics: Develop and optimize Power BI semantic models, DAX measures, and reports supporting commercial analytics (e.g., sales dashboards, HCP engagement tracking, inventory monitoring)
• AI data integration: Build and maintain MCP (Model Context Protocol) server integrations to connect enterprise data sources with AI solutions such as Copilot and Claude, enabling AI-assisted analytics and decision support
• Data governance: Support data quality, lineage documentation, and compliance with Customer’s data governance standards including data inventory and validation requirements
• Cross-functional collaboration: Work closely with business stakeholders, IT teams, and global data platform teams to translate data requirements into technical solutions and ensure alignment with enterprise architecture

Job Qualification:

• Experience: 3–5 years of hands-on data engineering experience, including ETL pipeline development, data modeling, and analytics platform support
• Technical skills: Proficiency in Microsoft Fabric (Data Factory, Dataflows Gen2, Lakehouse, SQL Analytics Endpoint), Power BI (semantic models, DAX, Power Query M), and SQL. Experience with Python for data transformation is a plus
• AI integration: Demonstrated experience or strong familiarity with MCP server architecture and integrating structured data sources with AI platforms (e.g., Copilot, Claude, or similar LLM-based tools)
• Domain background: Prior experience in the pharmaceutical or healthcare industry preferred; familiarity with commercial data (sales, HCP, consent) is an advantage
• Location: Prefer to work on-site / Remote working acceptable

Preferred Skills:

• Problem solver: Approaches data challenges methodically, identifies root causes in pipeline failures, and implements durable fixes
• Self-directed: Takes ownership of deliverables and proactively identifies data quality issues or optimization opportunities without waiting for direction
• Collaborative communicator: Translates complex technical concepts into clear language for business stakeholders and works effectively across bilingual (EN/KR) teams
• Continuous learner: Stays current with evolving data platform capabilities (e.g., Fabric updates, AI tooling advancements) and applies new techniques to improve existing solutions

Language Skills: Native Korean speaker with fluent English (business-level written and verbal communication required)

Company Description:

One of the world's leading professional services companies, transforming clients' business, operating and technology models for the digital era.

Their unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses.

Headquartered in the U.S., this company is one of the Fortune 500 companies and is consistently listed among the most admired companies in the world.

[Passive smoking measures]

Indoor smoking

Designated smoking area

. Skillset Required: Microsoft Fabric, Data Factory, Dataflows Gen2, Lakehouses, Power BI, semantic models, DAX measures, DAX, Power Query M, SQL, Python, MCP server architecture, AI integration, ETL pipeline development, data modeling, analytics platform support, data pipeline development, data transformation, AI-assisted analytics, data quality, data governance, data lineage documentation, compliance, cross-functional collaboration, bilingual communication