Data Engineer: End-to-End Pipelines & Quality

PartnerOne; Chile; Presencial; CLP 25.000.000 - 42.000.000; Jornada completa

Descripción de la vacante

PartnerOne is seeking a Data Engineer to design, build, and maintain data pipelines powering analytics and reporting for the Mortgage Cadence platform. The role emphasizes strong SQL, ETL, and data quality expertise, with experience in semantic modeling as a plus.

The ideal candidate will own end-to-end data workflows, implement incremental loads, and ensure data quality across ingestion, transformation, and output stages, collaborating with the Lead on complex patterns.

Formación

• Advanced SQL with optimization, window functions and debugging complex transformations.
• Experience with Microsoft Fabric Dataflow Gen2, Notebooks, Lakehouse or similar ETL tools.
• Strong relational DB design and dimensional modeling knowledge.
• Power Query / M for complex data shaping and transformations.
• Python or similar scripting for data manipulation and automation.
• Git basics for collaboration and versioning.
• Data quality frameworks including unit tests and validation rules.

Responsabilidades

• Design and build ETL pipelines using Microsoft Fabric and equivalent tools.
• Write optimized SQL for data ingestion and transformations.
• Implement data quality checks across pipelines and dashboards.
• Develop and maintain dimensional models and aggregation logic.
• Collaborate with Lead to refine transformation logic and semantic models.
• Troubleshoot pipeline issues and coordinate with IT/Engineering teams.

Conocimientos

Advanced SQL

Microsoft Fabric

ETL pipelines

Data modeling

Power Query

Python

Git

Data quality testing

Herramientas

SQL Tools

dbt

Talend

Informatica

Descripción del empleo

PartnerOne is seeking a Data Engineer to design, build, and maintain data pipelines powering analytics and reporting for the Mortgage Cadence platform. The role emphasizes strong SQL, ETL, and data quality expertise, with experience in semantic modeling as a plus.

The ideal candidate will own end-to-end data workflows, implement incremental loads, and ensure data quality across ingestion, transformation, and output stages, collaborating with the Lead on complex patterns.