Lead Data Engineer

Job summary

LEAD DATA ENGINEER We are Len*** Len*** 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 Assoc

Benefits

Qualifications

• 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:

Responsibilities

• on the Team
• Design, Build, and Operationalize: Formulate production-grade data engineering solutions for Len***’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
• of the Associates.
• and activities may change at any time with or without notice.
• LI-KB2

Skills

Degrees

Travel

Industry

Company size