Position Overview
We are seeking an experienced Data Engineer to support the client team in designing, developing, and maintaining scalable data pipelines and data solutions supporting U.S. mortgage operations and analytics. The ideal candidate will have strong hands-on experience with Snowflake, modern data engineering practices, and a solid understanding of the U.S. mortgage/lending industry.
The Data Engineer will work closely with business stakeholders, data analysts, application teams, and other engineers to integrate, transform, validate, and deliver high-quality mortgage data for reporting, analytics, operational processes, and downstream applications.
Key Responsibilities:
Design, develop, and maintain scalable ETL/ELT data pipelines for U.S. mortgage and lending data.
Develop and optimize data solutions using Snowflake as the enterprise cloud data platform.
Ingest and integrate data from multiple sources, including mortgage loan origination, servicing, financial, customer, property, and third-party data sources.
Develop complex SQL queries, stored procedures, views, tables, and data transformations in Snowflake.
Build reliable batch and/or near-real-time data pipelines using modern data engineering tools and frameworks.
Perform data cleansing, transformation, reconciliation, validation, and quality checks.
Work with mortgage-domain datasets such as loan applications, borrower information, loan terms, property information, underwriting, servicing, payments, escrow, interest rates, and loan statlifecycle data.
Translate business and mortgage-domain requirements into scalable technical data solutions.
Optimize Snowflake workloads for performance, scalability, reliability, and cost.
Develop reusable data models and curated datasets for analytics and reporting.
Troubleshoot data pipeline failures, data quality issues, and production incidents.
Implement appropriate data security, access controls, and governance practices for sensitive mortgage and customer data.
Collaborate with Data Architects, Data Scientists, Business Analysts, QA teams, and application developers.
Document data pipelines, source-to-target mappings, data models, transformation logic, and operational procedures.
Participate in code reviews, testing, deployment, and production support activities.
Required Qualifications:
5+ years of experience in Data Engineering or a related field.
Strong hands-on experience with Snowflake.
Strong proficiency in SQL, including complex queries, joins, CTEs, window functions, performance tuning, and data transformation.
Experience developing ETL/ELT pipelines and integrating data from multiple sources.
Strong understanding of data warehousing concepts, dimensional modeling, data lakes/lakehouses, and data integration.
3+ years of experience working with U.S. mortgage, lending, banking, financial services, or related datasets.
Understanding of the U.S. mortgage lifecycle, including loan origination, underwriting, closing, servicing, payments, and loan status.
Experience working with large-volume datasets and production data pipelines.
Strong understanding of data quality, data validation, reconciliation, and governance.
Experience with Git or other source-control systems.
Strong analytical and problem-solving skills.
Preferred Qualifications:
Experience with Python for data engineering, automation, or data processing.
Experience with AWS, Azure, or Google Cloud Platform cloud environments.
Experience with Apache Spark, Databricks, Airflow, dbt, Kafka, or similar modern data engineering technologies.
Experience with Snowflake features such as Streams, Tasks, Snowpipe, Dynamic Tables, Time Travel, and Zero-Copy Cloning.
Experience implementing CI/CD for data pipelines.
Familiarity with mortgage industry data standards and sources such as Fannie Mae, Freddie Mac, MISMO, credit, property, and servicing data.
Experience with mortgage servicing or loan-origination platforms is highly desirable.
Experience supporting enterprise data warehouses and production analytics environments.
Mortgage Domain Knowledge
The successful candidate should understand common U.S. mortgage concepts and data elements, including:
Mortgage loan origination and servicing
Borrower and co-borrower information
Loan application and underwriting data
Loan amount, interest rate, term, and payment information
Property and appraisal data
Closing and funding
Escrow and insurance
Principal and interest payments
Delinquency and default indicators
Loan status and lifecycle events
Conventional, FHA, VA, and other mortgage products
Mortgage servicing and investor-related data
Mortgage reporting and regulatory data
Technical Environment:
Primary: Snowflake, SQL, ETL/ELT, Data Warehousing
Programming: Python and/or other scripting languages
Cloud: AWS / Azure / Google Cloud Platform
Data Engineering: Spark, Databricks, dbt, Airflow or comparable technologies
Source Control: Git
Domain: U.S. Mortgage / Lending / Financial Services
Education
Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline, or equivalent professional experience.
Key Success Factors
The ideal candidate is a hands-on Data Engineer who combines strong Snowflake/data engineering expertise with practical U.S. mortgage-domain knowledge. The candidate should be comfortable working with complex mortgage datasets, understanding business requirements, and converting those requirements into reliable and scalable data pipelines for the Livegage team.
Regards,
Shawn Davis,
Pull skill Technologies Inc.
Email ID :
Phone no:
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