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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