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Data Engineer

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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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