Data Engineer

Responsibilities Design, Build, and Operationalize: Formulate production-grade data engineering solutions for Lennar’s data and analytics platforms and products. Pipeline Architecture: Architect and implement reliable ETL, ELT, and streaming data ingestion/delivery processes across multiple enterprise sources. Modern Python Development: Develop, maintain, and containerize modular data applications and utility scripts using Python, leveraging modern cloud infrastructure. Scale and Improve Infrastructure: Improve data ingestion architecture, emphasizing data quality, cost-performance, maintainability, and extensibility across storage and compute layers. Enforce Standards and Downstream Integrity: Define and implement engineering standards for the data team (including code modularization, version control, automated testing, and secure CI/CD workflows). Ensure strict guidelines for schema evolution to safeguard downstream analytics from unilateral changes. Platform Observability: Instrument data analytics platforms with robust metrics, alerting, and automated monitoring (SLAs/SLOs) to ensure high availability and data trustworthiness. AI-Driven Productivity: Leverage modern AI-assisted development tools within daily engineering workflows to accelerate code generation, optimize heavy queries, and improve overall delivery speed. AI/ML Integration: Collaborate with data science teams to design and optimize data layers specifically tailored for Generative AI applications, Retrieval-Augmented Generation (RAG), and LLM frameworks. Ecosystem Integration: Wrangle and integrate data from highly disparate production systems to allow data analysts and data scientists to leverage optimized, end-to-end data products. Business Alignment: Gain a deep understanding of core business processes and align technical data development with strategic business objectives. Your Toolbox Core Expertise (8+ years preferred) in: Data Architecture & Enterprise Modeling: Advanced data warehousing concepts, cloud data lakes, and structured multi-layer designs (Bronze, Silver, Gold). Advanced Data Transformations: Designing complex operational pipelines, data cleanup, and robust standardization strategies. Production SDLC & Workflow Best Practices: Rigorous code reviews, end-to-end testing/QA methodologies, and resilient error-handling frameworks. Data Governance & Security: Implementing enterprise-level role-based access controls (RBAC), data compliance, and secure environments. Strong Technical Experience (3+ to 6+ years) with: Advanced Python Development: Writing clean, object-oriented, and production-grade Python code for complex data manipulation, automation, and API communication. Hands-on experience deploying, managing, and scaling containerized data workloads using AWS ECS (Elastic Container Service) and ECR. Core AWS architecture: S3, IAM, Lambda, EC2, CloudWatch, and CloudTrail. #J-18808-Ljbffr