Senior Data Engineer

Saaf Finance; India; Remote; INR 1,800,000 - 3,000,000; Full time

Benefits offered by this job

Remote-first with flexible hours

Professional development budget up to

Home office setup

Job summary

Saaf AI is redefining mortgage data engineering with scalable pipelines, AI-embedded data models, and real-time workflows. You will design and optimize ETL/ELT pipelines, build data warehouses, and collaborate with product managers, data scientists, and full-stack engineers to deliver high-quality data for underwriting and analytics.

The role emphasizes hands-on, fast-paced work in a remote-first setup with a strong focus on data security and regulatory compliance.

Qualifications

• 5+ years in a data engineering or similar backend data-focused role.
• Strong SQL and Python development for data transformation.
• Experience with dbt and modern ETL/ELT frameworks.
• Cloud experience (AWS preferred) and serverless data services.
• Snowflake or similar data warehouse experience.
• Experience with API integrations and data ingestion.
• Experience modeling data (Kimball/Star schema, Data Vault).
• Familiarity with AI-powered development tools for pipelines.
• Experience with CI/CD for data workflows.
• Experience with financial datasets and high compliance.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines for structured and unstructured data.
• Leverage AI-assisted tools to accelerate pipeline authoring and automate boilerplate code.
• Build and optimize data warehouses and marts (Snowflake/BigQuery) for analytics.
• Design data models (conceptual, logical, physical) for scalable datasets.
• Ingest data from APIs, SaaS platforms, and internal systems into the core platform.
• Build connectors and ingestion frameworks handling schema evolution and errors.
• Implement data validation, schema governance, and documentation.
• Use AI tools for data profiling, anomaly detection, and lineage docs.
• Use AI-assisted coding, testing, and automation in workflows.
• Evaluate and integrate emerging AI tools into data development.
• Build agentic workflows and multi-step automated processes in real time.
• Apply AI-assisted analysis to debug pipelines and optimize queries.
• Monitor and tune pipeline and warehouse performance for cost efficiency.
• Set up logging, monitoring, and alerts for data jobs.
• Enforce data security and regulatory compliance with traceability.
• Collaborate with product managers, data scientists, and engineers.
• Foster security-first mindset across data operations.

Skills

SQL

Python

dbt

AWS

Snowflake

API integrations

Data modeling

CI/CD for data

AI-powered tooling

Prompt engineering

Tools

Cursor

GitHub Copilot

Claude Code

CI/CD pipelines

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

• Competitive salary
• Unlimited PTO
• Remote-first with flexible hours
• Upto $2,000/year professional development budget
• Home office setup