About This Role
Saaf AI is building the future of mortgage lending by combining cutting-edge AI with robust data infrastructure. As part of a top-10 private lender processing billions in loan volume, backed by leading asset managers and funds, we are growing fast — and data and AI are at the center of everything we build.
We don't just experiment with AI — we integrate it deeply into how we operate. Our systems rely on scalable data pipelines, structured data models, and real-time workflows that power underwriting, document processing, and borrower interactions. AI is embedded across these layers, from data extraction and validation to intelligent automation.
If you're excited about building high-quality data systems in an AI-native environment — where data pipelines, automation, and intelligent workflows come together — you'll fit right in.
Key Responsibilities
Data Pipeline Development
- Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources.
- Leverage AI-assisted development tools to accelerate pipeline authoring, generate transformation logic, and automate boilerplate code.
Data Warehousing & Modeling
- Build and optimize data warehouses and marts (Snowflake, BigQuery, or similar) for analytics, reporting, and product use cases.
- Design, implement, and maintain conceptual, logical, and physical data models to ensure scalable, consistent, and high-quality datasets for downstream analytics and applications.
Integration & Ingestion
- Ingest data from APIs, SaaS platforms (CRM, financial data APIs), and internal systems into the core data platform.
- Build and maintain reliable connectors and ingestion frameworks that handle schema evolution, rate limits, and error recovery.
Data Quality & Governance
- Implement validation, schema management, and robust documentation to ensure data accuracy and compliance.
- Use AI tools to support data profiling, anomaly detection, and automated documentation of data lineage and transformations.
AI-Integrated Data Engineering
- Use AI-assisted tools (code generation, intelligent autocomplete, automated testing) as a regular part of your data engineering workflow.
- Evaluate and integrate emerging AI tools and practices into the team's data development process.
- Build and support agentic workflows and multi-step automated processes that act on data in real time, including AI-powered data validation and enrichment.
- Apply AI-assisted analysis to debugging pipeline failures, optimizing query performance, and identifying data quality issues.
Performance & Reliability
- Monitor and fine-tune pipeline and warehouse performance for scalability and cost efficiency.
- Set up logging, monitoring, and alerting for data jobs to ensure reliability and fast incident response.
Security & Compliance
- Apply data security and privacy controls aligned with financial regulatory requirements, ensuring full traceability of every transformation.
- Foster a security-first mindset across all data operations.
Analytics Enablement
- Provide clean, consistent datasets for analysts, product managers, and operational teams to support fast, data-driven decisions.
- Collaborate closely with product managers, data scientists, and full stack engineers to align data models with business needs.
Qualifications
Required
- 5+ years in a data engineering or similar backend data-focused role.
- 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.
- Proficient in data modeling (Kimball/Star schema, Data Vault).
- Demonstrated, regular use of AI-powered development tools (e.g., Cursor, GitHub Copilot, Claude Code, or similar) to accelerate data pipeline development, debugging, or documentation.
- Proven track record of delivering production-grade data pipelines at scale.
- Experience implementing CI/CD practices for data workflows.
- 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.
Preferred
- 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 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.
- Experience with prompt engineering for code generation, data transformation logic, or building AI-powered data workflows.
Benefits
- Competitive salary
- Unlimited PTO
- Remote-first with flexible hours
- Upto $2,000/year professional development budget
- Home office setup stipend
