Lead Data Engineer

Lennar•Irving, TX

17h

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. This position sits in our Enterprise Data and Analytics team, which aims to drive improved business outcomes using insights gleaned from data and analytics, infusing them into Lennar’s corporate fabric. The Lead Data Engineer will provide technical leadership to our data platform engineering 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. A career with purpose. A career built on making dreams come true. A career built on building zero defect homes, cost management, and adherence to schedules.

Requirements

Expertise (At least 8+, prefers 12+ years) in: Data Architecture design
Data modeling & Data warehousing concepts
Data transformations and standardizations
ETL processes & strategies
Monitoring and error handling
SDLC & workflow best practices
Code Reviews
QA/Testing methodologies
Strong experience (At least 3+, prefers 6+ years) with the following technologies and platforms:
AWS platform: S3, EC2, EMR, EKS, Glue, Lambda, AppFlow, Cloudwatch etc.
AWS certification is big plus
Snowflake Data Cloud Account Administration
Virtual warehouse strategies
Snowflake feature implementation: Data Sharing, Time Travel, and Zero-copy cloning
Role-based Access Control strategies
Dbt
Managing dbt cloud environment
Managing multi-repository dbt projects
Creating and managing dbt models
Creating and leveraging dbt macros
Version control & branching strategies (Github a plus)
Proficient in languages: SQL, Python
Data governance, security, and compliance concepts
Data Ingestion
Incremental and CDC ingestion methods
REST APIs
Familiarity (At least 1+ years of experience) with:
Orchestration & scheduling tools (Prefect, Airflow is a plus)
Qlik Replicate
Ability to work collaboratively and productively with other team members to achieve Lennar’s objectives.
Thirst to help transform Lennar into an insights-driven organization.
Demonstrated some experience in all aspects of development including, but not limited to, gathering requirements, development of technical components related to process scope and supporting testing and post implementation support.
Ability to work and partner with users and stakeholders to gather solution requirements.
Experience working with business users to understand how to optimally deliver insights within their operational workflows & decision-making processes.
Ability and willingness to quickly learn new technologies.
Ability and willingness to learn about the business, its strategy, objectives, and core business processes.
Travel up to 10% of the time to Divisions within the Lennar family.
Interact well with co-workers.
Cross train for position(s) within the team organizational structure from time to time, as required by the Leadership Team.
Comply with and implement company policies and procedures.
Accept constructive criticism.
Strong work ethic.
Team player.

Responsibilities

Design, build, and operationalize data engineering solutions for Lennar’s data and analytics platforms and products.
Architect and implement ETL, ELT and streaming data ingestion data delivery processes across multiple sources.
Experience in data modeling, cloud data lake, cloud data warehouse
Instrument data analytics platforms with robust metrics and monitoring.
Improve data ingestion architecture, emphasizing data quality, maintainability, and extensibility.
Support process improvement on the team to enable rapid development of data products.
Define and Implement standards and best practices for data analytics team, including code modularization, versioning, testing, automation of CI/CD workflows, code reviews etc.
Gain an understanding of core business processes and align data development with business strategy.
Wrangle and integrate data from disparate systems to allow data analysts and data scientists to leverage end-to-end data and information.