Ingenierio de datos data fabric

Descripción del cargo

Data Engineer
Requirements
• 3+ years of hands-on experience with Microsoft Fabric technology suite and Power BI.
• Strong experience designing and supporting:
Data Pipelines
Semantic Models
Dataflows
Lakehouse and Direct Lake
Notebooks and Gateway
• Proven experience in Lakehouse performance tuning and troubleshooting.
• Experience with Python and/or PySpark for data transformation and automation.
• Proficiency in SQL, including performance tuning for large datasets.
• Solid understanding of medallion architecture and lakehouse principles.
• Strong knowledge of Power BI data modeling, including: DAX, M / Power Query, Semantic modeling best practices
• Familiarity with data connectors (databases, APIs, OData).
• Experience with cloud-based data environments, preferably Azure.

Responsibilities
• Design and build data pipelines and transformations within Microsoft Fabric, including:
Semantic Models, Dataflows Gen1/Gen2, Lakehouse and Direct Lake, Pipelines and Notebooks
• Optimize Lakehouse performance through:
Efficient table design (partitioning, V/Z-ordering, clustering)
File size management, compaction, and vacuum strategies
Query tuning across Spark, SQL, and Fabric Warehouse engines
• Diagnose and resolve performance issues across ingestion, transformation, and serving layers.
• Implement monitoring and metrics for data latency, pipeline throughput, and table performance.
• Develop modular, reusable Python/PySpark code for data ingestion, transformation, validation, and automation.
• Build Python-based utilities for metadata management, orchestration support, and automated data quality checks.
• Implement medallion architecture (Bronze, Silver, Gold) to deliver clean, reliable, analytics-ready datasets.
• Create standardized and well-modeled data tables for analytics, reporting, and Power BI consumption.
• Partner with Power BI engineers to deliver trusted, high-performing datasets supporting investment, risk, and operations teams.
• Ensure alignment with Power BI best practices, including DAX, Power Query, star schema modeling, and incremental refresh.
• Implement data quality rules, schema validation, and monitoring across the data lake.
• Leverage Fabric automation for scheduling, orchestration, and continuous operations.
• Contribute to metadata management, lineage, access control, and data governance standards.
• Support CI/CD pipelines and source control practices for Fabric and Power BI assets.
Language
Advanced 80-95%
Location
Colombia