Data Engineer

Description

The Data Engineer operates within the framework established by the Lead — designing, building, and maintaining robust data pipelines and transformation logic that power analytics, compliance, and operational reporting across the Mortgage Cadence Platform.

The role is execution-focused with increasing ownership of end-to-end data workflows as familiarity with the platform grows. Strong SQL, ETL, and data quality skills are required; the ability to build reports and leverage semantic models is secondary to data engineering excellence.

Job 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

Requirements

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

Mortgage/lending domain familiarity preferred; willingness to learn domain required

Works effectively within defined standards and escalates architectural questions to Lead

Able to balance speed with quality; advocates for technical excellence