Data Engineer — Lakehouse, PySpark & Power BI

Contour Software; Lahore; On-site; PKR 1,200,000 - 2,400,000; Full time

Benefits offered by this job

Market-leading salary

Medical coverage

Provident Fund

Performance-based bonuses

Lunch/Dinner

Professional development budget

Leave encashment

Job summary

Contour Software is seeking a Data Engineer to maintain and extend a Microsoft Fabric Lakehouse platform. You will build and optimize data pipelines, transformations, and reporting solutions, working within an agile team to deliver high-quality data and analytics capabilities.

The role focuses on extending a multi-tenant lakehouse, optimizing Fabric pipelines, PySpark notebooks, and Power BI semantic models, while ensuring data quality and reliability in production environments.

Qualifications

• 2+ years of experience with multi-tenant analytics systems or lakehouses
• Experience with Microsoft Fabric (or similar modern data platforms)
• Proficient in PySpark notebooks and Power BI
• Knowledge of data modeling and ETL/ELT patterns

Responsibilities

• Extend and optimize a multi-tenant lakehouse in Microsoft Fabric
• Build and maintain Fabric pipelines (ingestion, orchestration, monitoring)
• Develop and optimize PySpark notebooks for data transformation
• Create and enhance Power BI reports and semantic models
• Ensure data quality, performance, and reliability
• Collaborate with Product and QA to deliver tested, production-ready solutions

Skills

Data modeling

ETL/ELT

Distributed data processing

Multi-tenant analytics

Tools

Microsoft Fabric

PySpark

Power BI

Oracle databases

Job description

Contour Software is seeking a Data Engineer to maintain and extend a Microsoft Fabric Lakehouse platform. You will build and optimize data pipelines, transformations, and reporting solutions, working within an agile team to deliver high-quality data and analytics capabilities.

The role focuses on extending a multi-tenant lakehouse, optimizing Fabric pipelines, PySpark notebooks, and Power BI semantic models, while ensuring data quality and reliability in production environments.