Lead Data Engineer

Requirements

• 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.
• AWS Platform & Containerization: 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.
• AWS Certification is a strong plus.
• Snowflake Data Cloud: Account administration, optimal virtual warehouse clustering strategies, and budget-optimization.
• Expert feature implementation: Data Sharing, Time Travel, and Zero-copy cloning.
• dbt (Data Build Tool): Managing multi-repository dbt projects and configuring dbt Cloud environments.
• Creating, documenting, and optimizing advanced dbt models and custom macros.
• AI-Assisted Engineering & Data Tools: Daily proficiency with AI coding assistants (GitHub Copilot, Cursor, or Claude/OpenAI APIs) to maximize development efficiency.
• Familiarity with cloud-native AI services (e.g., Snowflake Cortex or AWS Bedrock) for embedding LLM capabilities directly inside the data layer.
• Exposure to frameworks used for AI data preparation (e.g.,

Additional Information

Lead Data Engineer We are Lennar Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live in, and fostering a culture of opportunity and growth for our Associates throughout their career. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States. A Career that Empowers You to Build Your Future The primary mission of the Lead Data Engineer role is to help our business evolve into a data and insights-driven organization. The Lead Data Engineer will provide technical leadership to our Product Team. This is done by helping design and implement our next generation data and analytics platforms and products using Data engineering best practices. The Lead Data Engineer also will implementing engineering solutions along with the team. In addition, this person focuses on empowering and enabling our business users through self-service and automation. The Lead Data Engineer is a key role in operationalizing Lennar's enterprise data fabric. Your Responsibilities on the Team 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.

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