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;