Lead Data Engineer

Lennar•Waterford, FL

1d•Onsite

About The Position

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.

Requirements

Advanced data warehousing concepts, cloud data lakes, and structured multi-layer designs (Bronze, Silver, Gold).
Designing complex operational pipelines, data cleanup, and robust standardization strategies.
Rigorous code reviews, end-to-end testing/QA methodologies, and resilient error-handling frameworks.
Implementing enterprise-level role-based access controls (RBAC), data compliance, and secure environments.
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.
Account administration, optimal virtual warehouse clustering strategies, and budget-optimization.
Expert feature implementation: Data Sharing, Time Travel, and Zero-copy cloning.
Managing multi-repository dbt projects and configuring dbt Cloud environments. Creating, documenting, and optimizing advanced dbt models and custom macros.
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., LangChain, Vector Databases, or text embedding generation).
Incremental loading and Change Data Capture (CDC) methods.
Extensive experience querying and integrating with complex external REST APIs.
Advanced Git/GitHub branching strategies, pull request enforcement, and automated deployment pipelines.
Orchestration & Scheduling: Tools like Prefect, Airflow, or similar modern workflow management software.
Data Replication: Enterprise tools such as Qlik Replicate.
A "Product-First" Mindset: Demonstrated ability to partner directly with business users and stakeholders to gather solution requirements and translate them into technical assets.
Collaborative Drive: Ability to work productively across parallel tracks (Software, Devops, Data) to achieve corporate objectives while protecting engineering quality.
Technical Curiosity: A sharp mind and willingness to quickly master new technologies, architectures, and changing business domains.

Nice To Haves

8+ years preferred in Core Expertise areas.
3+ to 6+ years of Strong Technical Experience.
AWS Certification is a strong plus.
Familiarity (1+ years of experience) with Orchestration & Scheduling tools like Prefect, Airflow, or similar modern workflow management software.
Familiarity (1+ years of experience) with Data Replication enterprise tools such as Qlik Replicate.

Responsibilities

Formulate production-grade data engineering solutions for Lennar’s data and analytics platforms and products.
Architect and implement reliable ETL, ELT, and streaming data ingestion/delivery processes across multiple enterprise sources.
Develop, maintain, and containerize modular data applications and utility scripts using Python, leveraging modern cloud infrastructure.
Improve data ingestion architecture, emphasizing data quality, cost-performance, maintainability, and extensibility across storage and compute layers.
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.
Instrument data analytics platforms with robust metrics, alerting, and automated monitoring (SLAs/SLOs) to ensure high availability and data trustworthiness.
Leverage modern AI-assisted development tools within daily engineering workflows to accelerate code generation, optimize heavy queries, and improve overall delivery speed.
Collaborate with data science teams to design and optimize data layers specifically tailored for Generative AI applications, Retrieval-Augmented Generation (RAG), and LLM frameworks.
Wrangle and integrate data from highly disparate production systems to allow data analysts and data scientists to leverage optimized, end-to-end data products.
Gain a deep understanding of core business processes and align technical data development with strategic business objectives.