• 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

• Develop and maintain an OLAP ETL project using PL/SQL, AWS Glue (Python), RedShift • Support Java and Data Science development teams as an Oracle database expert • Develop and support other SQL and PL/SQL code existing in the platform

Role 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. - 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 Qualifications - 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

• Leads projects for design, development and maintenance of a data and analytics platform. • Effectively and efficiently process, store and make data available to analysts and other consumers. • Works with key business stakeholders, IT experts and subject-matter experts to plan, design and deliver optimal analytics and data science solutions. • Works on one or many product teams at a time. • Designs and automates deployment of our distributed system for ingesting and transforming data from various types of sources (relational, event-based, unstructured). • Designs and implements framework to continuously monitor and troubleshoot data quality and data integrity issues. • Implements data governance processes and methods for managing metadata, access, retention to data for internal and external users. • Designs and provides guidance on building reliable, efficient, scalable and quality data pipelines with monitoring and alert mechanisms that combine a variety of sources using ETL/ELT tools or scripting languages. • Designs and implements physical data models to define the database structure. • Optimizing database performance through efficient indexing and table relationships. • Participates in optimizing, testing, and troubleshooting of data pipelines. • Designs, develops and operates large scale data storage and processing solutions using different distributed and cloud based platforms for storing data (e.g. Data Lakes, Hadoop, Hbase, Cassandra, MongoDB, Accumulo, DynamoDB, others). • Uses innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. • Assists with renovating the data management infrastructure to drive automation in data integration and management. • Ensures the timeliness and success of critical analytics initiatives by using agile development technologies such as DevOps, Scrum, Kanban Coaches and develops less experienced team members.