Our mission at LendingClub is to empower those who strive to achieve better 

financial health. Data intelligence team plays a crucial role in achieving our 

mission.

We are seeking a Lear Data Modeling Engineer for our Data team to provide data 

modeling, data processing, and pipeline orchestration capabilities. You’ll be part of 

the Data Intelligence organization that helps drive business decisions using data. 

You will have the opportunity to use your expertise in data modeling, software 

development, design thinking, and analytical skills to build scalable data pipelines, 

robust data models, and high-quality data products leveraging our PB-scale data.

This role is primarily focused on data modeling, with strong ownership of building 

and supporting the data pipelines and systems that operationalize those models. 

You will be responsible for designing and evolving data models in a centralized 

data warehouse, ensuring they are scalable, performant, and aligned with business 

needs. In parallel, you will contribute to engineering development to ensure 

reliable data ingestion, transformation, and delivery.

You will also play a key role in driving data standardization efforts, establishing 

consistent definitions, modeling patterns, and data quality practices across the 

organization.

What you’ll do:

• Design, develop, and maintain scalable, high-quality data models in a 

centralized data warehouse (primary focus) 

• Translate business requirements into well-structured, reusable data models 

that support analytics and downstream applications 

• Define and enforce data modeling standards, naming conventions, and best 

practices across domains 

• Lead and contribute to data standardization initiatives to ensure 

consistency and interoperability of data assets 

• Build and maintain data pipelines that support and operationalize data 

models (ingestion, transformation, and delivery) 

• Develop pipelines that transform raw data into clean, well-modeled, 

analytics-ready datasets 

• Collaborate with Product, Engineering, and Program teams to deliver endto-end data solutions (models + pipelines) 

• Optimize data models and pipelines for performance, scalability, and cost 

efficiency 

• Implement processes for data validation, quality monitoring, and reliability 

• Write high-quality, testable code; adopt TDD and contribute to engineering 

documentation and best practices 

• Perform root cause analysis on data and pipeline issues to improve system 

robustness 

About you:

• 10+ years of experience and a bachelor’s degree in Computer Science, 

Information Systems, or related field; or equivalent experience 

• Strong expertise in data modeling (dimensional modeling, warehouse 

design, normalization techniques) 

• Proven experience designing and implementing data models in 

centralized/cloud data warehouses (Databricks, Snowflake, Redshift, etc.) 

• Solid experience in data engineering and pipeline development to support 

modeled data layers 

• 5+ years of experience with Databricks and DBT 

• Experience with orchestration tools such as Airflow or Dagster 

• In-depth experience with distributed systems such as Spark 

• Strong programming skills (Java, Scala, Python) with experience building 

production-grade data pipelines 

• Experience with AWS cloud services (EC2, EMR, RDS) 

• Strong understanding of data standardization, data governance, and data 

quality best practices 

• Experience with Git, JIRA, Jenkins, and CI/CD pipelines 

• Experience working with cross-functional teams in a dynamic environment 

• You are passionate about data modeling and designing scalable, wellstructured datasets 

• You are equally comfortable implementing pipelines that bring those 

models to life 

• You think in terms of data consistency, reusability, and long-term 

maintainability 

• You value clean design, simplicity, and high-quality engineering practices 

• You have a track record of delivering reliable, well-modeled, production - ready data solutions

Responsibilities

Qualifications