Senior Data Engineer

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

Originally posted on Himalayas