Senior Data Engineer

AHL - Saaf AI

United States

Remote

USD 120,000 - 190,000

Full time

7 days ago

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Benefits offered by this job

Unlimited PTO

Remote-first with flexible hours

Development budget

Home office stipend

Job summary

Saaf AI in the United States is seeking a senior data engineer to design and maintain scalable data pipelines and AI-enabled workflows across underwriting, data validation, and reporting.

You will collaborate with product, data science, and engineering teams to ensure high-quality datasets, robust governance, and fast insights, while leveraging modern cloud technologies.

Qualifications

5+ years in a data engineering or backend data-focused role.
Strong SQL and Python development for data transformation.
Experience with ETL/ELT frameworks like dbt.
Proficiency with AWS and serverless data services.
Expertise in data warehousing (Snowflake) and data modeling.
Experience building production-grade data pipelines at scale.
Familiarity with CI/CD for data workflows.
Experience collaborating with product managers, data scientists, and engineers.
Startup mindset: hands-on, resourceful, fast-paced.
Experience with AI-powered development tools for data work.

Responsibilities

Design, implement, and maintain ETL/ELT pipelines from diverse sources.
Build and optimize data warehouses and data models for analytics.
Ingest data from APIs and SaaS platforms into the data platform.
Ensure data quality, governance, and lineage documentation.
Apply AI-assisted tooling to accelerate development and debugging.
Monitor performance, reliability, and cost of data systems.

Skills

SQL & Python

dbt

AWS

Snowflake

API integrations

Data modeling (Kimball/Star schema)

AI tooling for data

CI/CD for data workflows

Production-grade pipelines

Cross-functional collaboration

Tools

Cursor

GitHub Copilot

Claude Code

Job description

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