[Remote] Data Engineer

Note: The job is a remote job and is open to candidates in USA. PartnerOne is seeking a Data Engineer to design, build, and maintain robust data pipelines for the Mortgage Cadence Platform. This role focuses on executing end-to-end data workflows and requires strong SQL, ETL, and data quality skills.

Responsibilities

• Design and build extraction, transformation, and loading (ETL) pipelines using Microsoft Fabric (Dataflow Gen2, Notebooks, or equivalent tools)
• Write optimized SQL queries and transformations for data ingestion from designated source systems
• Apply data quality rules and validation logic at each pipeline stage
• Implement incremental loads and manage refresh schedules for performance
• Escalate to Lead for architectural decisions or complex transformation patterns
• Define and implement data quality checks at ingestion, transformation, and output stages
• Perform ongoing data validation to ensure pipeline outputs align with business logic and source system expectations
• Identify, document, and escalate data quality issues with root cause analysis
• Maintain data quality dashboards and SLA monitoring
• Support UAT for new data sources or transformation logic
• Build and maintain data transformations using Power Query, SQL, or Python as appropriate
• Develop dimensional models and define aggregation logic aligned with analytics requirements
• Optimize data structures for performance and maintainability
• Document transformation logic, lineage, and assumptions per team standards
• Troubleshoot pipeline failures and performance issues; coordinate resolution with IT/Engineering
• Respond to data discrepancy reports from business users and analysts
• Maintain documentation of data sources, data dictionaries, and transformation specifications
• Support capacity planning and optimization of Fabric environments and pipelines
• Collaborate with Lead to define semantic models and calculated metrics

Skills

• Strong SQL, ETL, and data quality skills
• Design and build extraction, transformation, and loading (ETL) pipelines using Microsoft Fabric (Dataflow Gen2, Notebooks, or equivalent tools)
• Write optimized SQL queries and transformations for data ingestion from designated source systems
• Apply data quality rules and validation logic at each pipeline stage
• Implement incremental loads and manage refresh schedules for performance
• Escalate to Lead for architectural decisions or complex transformation patterns
• Define and implement data quality checks at ingestion, transformation, and output stages
• Perform ongoing data validation to ensure pipeline outputs align with business logic and source system expectations
• Identify, document, and escalate data quality issues with root cause analysis
• Maintain data quality dashboards and SLA monitoring
• Support UAT for new data sources or transformation logic
• Build and maintain data transformations using Power Query, SQL, or Python as appropriate
• Develop dimensional models and define aggregation logic aligned with analytics requirements
• Optimize data structures for performance and maintainability
• Document transformation logic, lineage, and assumptions per team standards
• Troubleshoot pipeline failures and performance issues; coordinate resolution with IT/Engineering
• Respond to data discrepancy reports from business users and analysts
• Maintain documentation of data sources, data dictionaries, and transformation specifications
• Support capacity planning and optimization of Fabric environments and pipelines
• Collaborate with Lead to define semantic models and calculated metrics
• Advanced SQL — query optimization, window functions, performance tuning, debugging complex transformations
• Proficient with Microsoft Fabric — (Dataflow Gen2, Notebooks, Lakehouse) OR equivalent ETL tools (Python, dbt, Talend, Informatica)
• Strong understanding of relational database design and dimensional modeling
• Power Query / M — complex data shaping, merging, error handling, and transformation logic
• Python or similar scripting language — data manipulation, pipeline automation
• Git/version control basics — able to collaborate on code and track changes
• Data quality and testing frameworks — unit tests, assertions, validation rules
• Ability to interpret business requirements and design efficient data solutions
• Data governance mindset — understands data lineage, documentation, and quality standards
• Proactive about identifying edge cases and potential data issues
• Works effectively within defined standards and escalates architectural questions to Lead
• Able to balance speed with quality; advocates for technical excellence
• Mortgage/lending domain familiarity preferred; willingness to learn domain required

Company Overview