Senior Data Architect & Analytics Engineer

AHL - Saaf AI • New York, NY

54d

About The Position

We are building the future of mortgage lending by combining cutting-edge AI with proven lending operations. Saaf AI is an fintech startup now part of American Heritage Lending, a top-10 private lender processing billions in loan volume across Non-QM, DSCR, and conventional programs, and backed by some of the largest asset managers and funds. We are an AI-first team. Every engineer, every product decision, every workflow is designed around the question: “How does AI make this faster, smarter, or more reliable?” If you’re looking to push the limits of your expertise — using the latest AI tools and processes daily, not as an experiment but as your primary way of working — this role will put you at the bleeding edge of what data architecture and analytics engineering looks like in 2026 and beyond.

We’re hiring a Senior Data Architect & Analytics Engineer to own and evolve our analytics data platform. This is the first dedicated data hire — a hands-on builder who will shape how data powers every decision across underwriting, sales, and operations. The foundation is in place. But “foundation” is exactly the right word — the exciting, high-impact work is ahead. You’ll take ownership of a platform with enormous room to grow: building entity resolution across fragmented real estate data, designing enrichment pipelines that turn raw data into actionable intelligence, creating the semantic layer that makes AI-powered analytics possible, and evolving the architecture as we scale into new loan programs and data sources. This role is right for you if:
You’ve built and owned a modern analytics stack end-to-end — not just contributed to one
Data integrity isn’t something you think about after the fact — it’s the first thing you design for
You’re energized by building on a strong foundation and taking it somewhere ambitious
You want to be the foundational data person at a fast-growing company — high ownership, high impact, clear path to leading a team

Requirements

• dbt + Snowflake depth: 3+ years hands-on with dbt (models, tests, macros, documentation, Cloud environments) and Snowflake (data sharing, warehouses, roles, cost management). This is the core of the job.
• Advanced SQL: Query optimization, window functions, CTEs, incremental models — you think in SQL.
• Data quality obsession: You’ve built automated testing, validation, and monitoring into data platforms — not as an afterthought but as a design principle.
• Entity resolution or record linkage: Experience with probabilistic matching frameworks, or strong willingness to go deep quickly.
• Python for data engineering: Comfortable writing data processing scripts, pipeline tooling, and working with matching/ML libraries.
• Hands-on ownership: You write SQL, build pipelines, debug data issues, and own systems end-to-end. Not a diagram-only architect.
• Startup pace: Comfortable with ambiguity, able to prioritize pragmatically, and energized by building something from the ground up.

Nice To Haves

• Experience in fintech, real estate (property records, transaction histories, valuation data), lending, or financial services
• Experience with CDC / data replication tools (Airbyte, Fivetran, or similar)
• Familiarity with BI tools (Hex, Looker, Mode) and semantic layer concepts
• Container orchestration experience (ECS/Fargate or similar) for data pipelines
• Exposure to mortgage data or regulatory compliance (FCRA, GLBA, SOC2)
• Experience designing data models that support AI-driven processes — LLM-ready data structures, feature stores, rules engines

Responsibilities

• Own the production data pipeline end-to-end — ingesting external real estate and borrower data, transforming it through a layered model (staging → enrichment → business-ready marts), and keeping it reliable
• Build and maintain the semantic layer — model descriptions, metric definitions, and metadata that power BI dashboards, AI assistants, and self-serve analytics
• Manage warehouse infrastructure — roles, permissions, cost optimization, performance tuning
• Design and implement CDC replication to unify internal loan data with external enrichment sources, creating a single source of truth for borrower and property intelligence
• Build a production entity resolution pipeline — from deterministic exact matches to probabilistic fuzzy matching
• Calibrate match thresholds against real data and continuously improve recall and precision
• Design the feedback loop where operations teams validate matches, and those validations improve the model over time
• Evolve and extend the existing models — borrower experience scoring, portfolio analysis, entity relationship mapping, lead enrichment, and scoring
• Design new models as the business grows — underwriting packages, market intelligence, next-best-action recommendations, transaction timelines
• Extend the data architecture as new loan programs, data vendors, and internal systems come online — building for configuration-driven extensibility, not one-off code changes
• Treat data quality as a first-class product — automated testing, validation frameworks, monitoring dashboards, and alerting
• Own the governance playbook — naming conventions, schema versioning, lineage tracking, migration processes
• Ensure regulatory compliance in data handling (FCRA, GLBA) — borrower and property data in lending carries real legal obligations

Benefits

• AI-first, not AI-curious. We don’t use AI as a buzzword — it’s how we work. Engineers pair with AI daily. Product decisions are informed by AI analysis. If you’ve been waiting for a team that actually operates this way, this is it.
• Foundation laid, future wide open. The platform is just getting started — You’re not inheriting a finished system; you’re inheriting a launchpad.
• High ownership, real impact. as the first dedicated data architect hire.
• Mission that matters. Transform a $2 trillion industry and make homeownership more accessible. Your work directly impacts thousands of borrowers’ paths to homeownership.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed