Senior Data Engineer

Leni is an AI infrastructure platform built for asset managers and investors, designed to help individuals and organizations make confident, data-driven decisions. The platform is model-agnostic for most tasks and also runs specialized models trained on real estate and investment workflows, delivering highly accurate, verifiable results at lower cost than general-purpose AI. Leni has achieved top global performance on industry-relevant benchmarks such as DRACO, SpreadsheetBench, and GAIA, and consistently delivers near-perfect accuracy on user evaluations. By connecting to systems like Yardi, RealPage, Entrata, AppFolio, and Rent Manager, Leni builds a private context graph that captures the “how” and “why” behind every report, decision, and review. This context-aware memory layer powers insights, risk detection, and automated reporting that are fully traceable back to source data and aligned with real estate and investment needs.

Role Description

The Senior Data Engineer role at Leni is a full-time, hybrid position based in Toronto, ON, with flexibility for partial work from home. In this role, the Senior Data Engineer designs, builds, and maintains robust data pipelines and infrastructure to support Leni’s AI-driven real estate and investment products. Day-to-day activities include architecting and optimizing ETL workflows, modeling data from multiple property and investment systems, and developing scalable data warehouses and analytics layers that enable reliable, traceable insights. The Senior Data Engineer collaborates closely with product, engineering, and data science teams to ensure data quality, performance, and security across the platform, and to integrate new data sources into Leni’s context graph. This role also contributes to best practices, documentation, and mentoring, helping elevate data engineering standards across the organization.

Qualifications

• Strong Data Engineering skills, including building and maintaining data pipelines and distributed data systems.
• Proficiency in Data Modeling and relational/NoSQL database design to support analytics and AI-driven use cases.
• Experience with Extract Transform Load (ETL) processes and tools, including orchestration, monitoring, and optimization.
• Hands-on experience with Data Warehousing solutions and modern data stack technologies (e.g., cloud warehouses, data lakes).
• Applied Data Analytics skills, including working with large datasets to derive insights and support product and business decisions.
• Advanced proficiency in at least one programming language commonly used in data engineering (e.g., Python, Scala, or Java).
• Experience with cloud platforms and services (e.g., AWS, GCP, or Azure) and infrastructure-as-code concepts.
• Knowledge of real estate, asset management, or financial data domains is an asset.