Senior Data Engineer
Location
India
Posted
77 days ago
Salary
0
Seniority
Senior
Job Description
Senior Data Engineer
AHL - Saaf AI
Role Description 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 - 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 - Up to $2,000/year professional development budget - Home office setup stipend
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