Data Engineer

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Lime

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2017

🚗 Transport

🛍️ eCommerce

☁️ SaaS

💰 $418M Convertible Note on 2021-11

Transport • eCommerce • SaaS

Lime is a transportation company that provides shared electric scooters and bikes in urban areas, making it easy for users to travel short distances. With a focus on safety, sustainability, and community, Lime aims to offer an eco-friendly alternative to traditional transportation methods. Users can easily locate and rent vehicles through the Lime app, promoting responsible riding and parking practices to enhance the urban mobility experience.

📋 Description

• Design, build, and maintain high-throughput ETL/ELT pipelines for data ingestion, processing, and storage solutions. • Develop complex, performance-tuned data transformations using Python, high-performance SQL, and tools like dbt. • Contribute to our technical strategy and how we can scale to support future business needs • Implement data ops best practices, including CI/CD for data pipelines, version-controlled schemas (dbt), and automated testing. • Help drive data reliability and observability strategy, including improving data quality and lineage tracking. • Work with distributed processing systems like Spark, Flink, or Kafka to support scalable batch and real-time operational analytics. • Partner with the ML Platform team to prepare and provide clean, feature-rich datasets for model training and inference. • Ensure data stewardship by contributing to documentation, discoverability, and implementing robust data privacy and access controls.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field. • 2+ years of experience in data engineering and distributed systems. • Hands-on experience building and scaling data stacks on cloud providers (AWS preferred), including experience with Snowflake. • Expertise in developing and debugging complex data transformations using Python and high-performance SQL. • Experience with workflow orchestration tools such as Airflow. • Familiarity with distributed processing technologies like Spark, Flink, or Kafka. • Understanding of data modeling, ETL pipelines, and experience with data transformation tools like dbt. • Familiarity with modern data governance tools and practices (cataloging, lineage, and PII masking). • Experience with Iceberg, Debezium, or Infrastructure-as-Code tools like Terraform for managing data infrastructure.

🏖️ Benefits

• Offers Equity • Offers Bonus