Data Engineer

Data Engineer   INDIVIDUAL CONTRIBUTOR

Mortgage Cadence Platform (MCP/LOS)  |  Role Profile  |  Draft for Recruiting

Bottom line: 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.

CORE RESPONSIBILITIES

DATA PIPELINE DEVELOPMENT

• 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
  

DATA QUALITY & VALIDATION

• 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

TRANSFORMATION & MODELING

• 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
• Collaborate with Lead to define semantic models and calculated metrics

OPERATIONAL SUPPORT

• 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

REQUIRED SKILLS

Technical

• 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

Functional

• 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

COMMUNICATION REQUIREMENTS BY STAKEHOLDER

Stakeholder

Interaction Context

Communication Requirements

Analytics / BI Team

Data pipeline requirements, data quality issues, model design collaboration

• Translate analytical requirements into robust data solutions
• Communicate data lineage and transformation logic clearly
• Document assumptions and limitations of data sources and transforms
• Set realistic timelines for new pipelines or data source onboarding

Data Lead

Daily collaboration, code/design review, escalation of technical blockers

• Provide detailed status updates on assigned pipelines; flag performance or quality concerns early
• Document design decisions and trade-offs for Lead review — escalate architecture questions rather than assume
• Demonstrate commitment to code quality and maintainability; accept technical feedback constructively

IT / Engineering

Data access provisioning, source system clarifications, infrastructure support

• Communicate data requirements precisely — schema details, volume expectations, refresh frequency
• Escalate data access or infrastructure needs through Lead; provide business context
• Provide detailed defect reports with query examples and expected vs. actual results

Business / Operations

Data quality escalations, new data source requests

• Explain data quality issues and timelines in business terms; avoid over-technical language
• Ask clarifying questions about data requirements and business logic expectations
• Set expectations transparently; communicate delays or blockers early through Lead

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