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Remote Sr Data Engineer with mortgage exp-15+ years

SR Talent Solution IncUnited States🇺🇸United StatesPosted Sep 25, 2026

Why This Role Stands Out

Leverage your extensive data engineering expertise in this remote role, shaping critical mortgage industry data solutions with a focus on Snowflake and advanced SQL. If you have a strong background in U.S. lending and a passion for complex data challenges, this opportunity offers significant impact and the flexibility of remote work. Apply today to contribute your skills to this dynamic field!

Quick Overview

Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
18 hours ago
SQLAWSETLSnowflakeAirflowAzureDatabricksGitGoogle CloudKafkaPythondbt

Job Description

Title: Remote Sr Data Engineer-15+ years
Duration: 6 months/contract
Must have 3+ years of U.S. mortgage or lending-domain exp

Required Qualifications

  • 15+ years of data-engineering experience.
  • Strong hands-on Snowflake and advanced SQL expertise.
  • 3+ years of U.S. mortgage or lending-domain experience.
  • Experience with ETL/ELT, data warehousing, dimensional modeling, and large datasets.
  • Understanding of mortgage origination, underwriting, closing, servicing, payments, escrow, and loan lifecycle.
  • Experience with Python, Git, and AWS, Azure, or Google Cloud Platform.

Preferred

Experience with dbt, Airflow, Spark, Databricks, Kafka, CI/CD, and Snowflake Streams, Tasks, Snowpipe, or Dynamic Tables. Familiarity with MISMO, Fannie Mae, Freddie Mac, FHA, and VA mortgage data is highly desirable.

Client is seeking a Senior Data Engineer with strong Snowflake expertise and hands-on experience working with U.S. mortgage data. The consultant will design and maintain scalable data pipelines supporting mortgage operations, reporting, and analytics.

Key Responsibilities

  • Develop scalable ETL/ELT pipelines using Snowflake and SQL.
  • Integrate loan-origination, underwriting, servicing, borrower, payment, property, and third-party data.
  • Build data models and curated datasets for analytics and reporting.
  • Perform data validation, reconciliation, cleansing, and performance optimization.
  • Troubleshoot production pipelines and enforce data security and governance.
  • Collaborate with business, architecture, QA, and application teams.

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