[Hiring] Data Engineer @Nexiva

Role Description

We are looking for an experienced Data Engineer with strong hands-on Snowflake and SQL expertise and solid U.S. mortgage/lending domain knowledge. The Data Engineer will design, develop, and maintain scalable data pipelines and data solutions supporting mortgage operations, analytics, reporting, and downstream applications. The role will involve close collaboration with business stakeholders, analysts, architects, application teams, and other engineers.

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.
• Integrate data from mortgage origination, servicing, financial, customer, property, and third-party sources.
• Develop complex SQL queries, stored procedures, views, tables, and transformations.
• Perform data cleansing, validation, reconciliation, and quality checks.
• Build batch and/or near-real-time data pipelines.
• Develop reusable data models and curated datasets for analytics and reporting.
• Optimize Snowflake workloads for performance, scalability, reliability, and cost.
• Troubleshoot pipeline failures, data quality issues, and production incidents.
• Implement appropriate security, access controls, and governance for sensitive mortgage/customer data.
• Translate business and mortgage requirements into scalable technical data solutions.
• Participate in code reviews, testing, deployment, documentation, and production support.

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, data transformation, and performance tuning.
• Hands-on experience developing ETL/ELT pipelines and integrating multiple data sources.
• Strong understanding of data warehousing, dimensional modeling, data lakes/lakehouses, and data integration.
• 3+ years of experience with U.S. mortgage, lending, banking, financial services, or related datasets.
• Understanding of the U.S. mortgage lifecycle, including origination, underwriting, closing, servicing, payments, and loan status.
• Experience working with large-volume datasets and production data pipelines.
• Strong understanding of data quality, validation, reconciliation, and governance.
• Experience with Git/source control.
• Strong analytical and problem-solving skills.

Preferred Qualifications

• Python experience for data engineering, automation, or data processing.
• Experience with AWS, Azure, or GCP.
• Experience with Spark, Databricks, Airflow, dbt, Kafka, or similar 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 Fannie Mae, Freddie Mac, MISMO, credit, property, and servicing data.
• Experience with mortgage servicing or loan-origination platforms.
• Experience supporting enterprise data warehouses and production analytics environments.

Mortgage Domain Knowledge

• Mortgage origination and servicing
• Borrower/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/default indicators
• Loan status and lifecycle events
• Conventional, FHA, VA, and other mortgage products
• Investor and servicing data
• Mortgage reporting and regulatory data

Technical Environment

• Primary: Snowflake, SQL, ETL/ELT, Data Warehousing
• Programming: Python and/or scripting languages
• Cloud: AWS / Azure / GCP
• Data Engineering: Spark, Databricks, dbt, Airflow or comparable tools
• 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.

Ideal Candidate

The ideal candidate is a hands-on Data Engineer who combines strong Snowflake/data engineering expertise with practical U.S. mortgage-domain knowledge and can translate complex business requirements into reliable, scalable data solutions.