LendingClub is looking to hire a Data Engineer to help design, build, and optimize the data systems that enable batch processing, real-time streaming, pipeline orchestration, data lake management, and data cataloging to support their data products and leverage data at petabyte scale.
Requirements
- 3+ years of hands on experience with distributed data systems including Hadoop, Spark, Hive, Kafka, DBT, and Airflow/Dagster
- At least 1 year of production coding experience implementing data pipelines in Python
- Experience with public cloud platforms (AWS preferred)
- Experience with Databricks and/or Snowflake
- Skilled in Git, JIRA, Jenkins, and shell scripting
- Familiarity with Agile, test-driven development, and source control management
Responsibilities
- Build systems, core libraries, and frameworks that power batch and streaming Data and ML applications
- Work with modern data technologies such as Hadoop, Spark, DBT, Dagster/Airflow, Atlan, Trino, and platforms like Databricks, Snowflake, and AWS
- Build data pipelines that transform raw data into canonical schemas representing business entities and publish them into the Data Lake
- Implement internal process improvements including automation, performance optimization, cloud cost reduction, and infrastructure scalability
- Implement systems for data quality, observability, governance, and lineage
- Support operations by managing production environments, resolving issues, and driving root-cause analysis
- Write unit and integration tests, follow test-driven development practices, and contribute to documentation and engineering wikis
Other
- Bachelor’s degree or higher in Computer Science or related field; or equivalent experience
- Strong collaboration and communication skills; empathetic and effective at problem solving
- Committed to building simple, reliable, high-quality data pipelines that deliver business value
- Hybrid work model, in-office Tuesdays, Wednesdays, and Thursdays.
- Primarily PT time zone requirements, flexible working across time zones when necessary.
