Data Engineer

Saaf Finance is building the AI workforce for the mortgage industry. We are an AI startup integrated with a top-10 mortgage lender, American Heritage Lending (AHL). Together we are combining AHL’s 15+ years of mortgage origination expertise with the power of AI-native innovation to redefine what’s possible in mortgage lending.

This is a rare opportunity to join at a pivotal moment. You’ll build and own the data pipelines, warehouses, and analytics infrastructure that power AI-driven underwriting, automation, and real-time financial decision-making at scale, working directly with founders and engineering leadership to shape how AI transforms a $17 trillion industry.

As a Data Engineer you will be part of a focused product and technology team with the backing of a $10 Billion+ New York-based investment fund—giving us stability, scale and resources of an established institution with the speed, innovation and ownership of a startup.

If you thrive in fast-moving environments, enjoy working with complex data systems, and want to see your work power real financial decisions for thousands of borrowers, this role is for you.

• Data Pipeline Development – Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources.
• Data Warehousing – Build and optimize warehouses and marts (Snowflake, BigQuery, or similar) for analytics, reporting, and product use cases.
• Integration – Ingest data from APIs, SaaS platforms such as CRM and financial data APIs, and internal systems into the core data platform.
• Data Modeling: Design, implement, and maintain conceptual, logical, and physical data models to ensure scalable, consistent, and high-quality datasets for downstream analytics and applications
• Data Quality and Governance – Implement validation, schema management, and robust documentation to ensure data accuracy and compliance.
• Performance Optimization – Monitor and fine-tune pipeline and warehouse performance for scalability and cost efficiency.
• Security and Compliance – Apply data security and privacy controls aligned with financial regulatory requirements, ensuring full traceability of every transformation.
• Analytics Enablement – Provide clean, consistent datasets for analysts, product managers, and operational teams to support fast, data-driven decisions.
  

Technical Expertise

• Strong SQL and Python development skills for data transformation and automation.
• Experience with modern ETL/ELT frameworks such as dbt.
• Proficiency with cloud platforms (AWS preferred) and serverless data services.
• Strong experience with data warehouse technologies (Snowflake preferred).
• Skilled in API integrations and ingestion from third-party systems.
  

Data Operations

• Proficient in data modeling (Kimball/Star schema, Data Vault).
• Experience implementing CI/CD practices for data workflows.
• Ability to set up logging, monitoring, and alerting for data jobs.

• Experience building agentic workflows and orchestrating multi-step automated processes that act on data in real time.
• Familiarity with data engineering patterns and infrastructure required for the recent wave of AI-powered tools and automation platforms.
• Experience working with financial datasets and APIs in a high-compliance environment.
• Understanding of data privacy regulations such as GDPR and CCPA.
  

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 3+ years in a data engineering or similar backend data-focused role.
• Proven track record of delivering production-grade data pipelines at scale.
• Experience collaborating closely with product managers, data scientists, and full stack engineers.
• Startup mindset: hands-on, resourceful, and comfortable operating in a fast-paced environment.

• Competitive salary
• High ownership from day one — your work will directly shape core systems and products
• Fast-paced environment with quick decision cycles and minimal bureaucracy
• Remote-first team with flexibility on work hours and location
• Direct access to founders and cross-functional teams — no layers, no silos
• Clear expectations, regular feedback, and support for professional growth
• Work on real problems in a complex, high-impact industry