Lead Data Platform Engineer
6 days ago
The estimated salary for this role is $190,000 - $270,000 per year.
Job DescriptionLennar, a Fortune 500 company, is looking for a highly experienced Lead Data Platform Engineer to join its Enterprise Data and Analytics team. As a key member of the organization, you will be responsible for designing and implementing next-generation data and analytics platforms and products using best practices in data engineering.
You will provide technical leadership to the data platform engineering team, help design and implement data engineering solutions, and empower business users through self-service and automation. Your primary mission will be to help Lennar evolve into a data and insights-driven organization, driving improved business outcomes using insights gleaned from data and analytics.
In this role, you will have the opportunity to work collaboratively with other team members to achieve Lennar's objectives, thirst to help transform Lennar into an insights-driven organization, and demonstrate experience in all aspects of development, including gathering requirements, developing technical components, supporting testing, and post-implementation support.
You will be required 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 and decision-making processes, ability and willingness to quickly learn new technologies, and ability and willingness to learn about the business, its strategy, objectives, and core business processes.
You will have the opportunity to work on designing and implementing ETL, ELT, and streaming data ingestion data delivery processes across multiple sources, architecting and implementing data models, cloud data lakes, and cloud data warehouses, instrumenting data analytics platforms with robust metrics and monitoring, improving data ingestion architecture, emphasizing data quality, maintainability, and extensibility.
Requirements- Expertise 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 with AWS platform: S3, EC2, EMR, EKS, Glue, Lambda, AppFlow, Cloudwatch etc., 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 with Orchestration & scheduling tools (Prefect, Airflow is a plus), Qlik Replicate
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