Senior 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

• Own the end-to-end design, delivery, and operation of major data products, including high-throughput ETL/ELT pipelines, transformation workflows, and storage solutions. • Partner with engineering, product, and business stakeholders to clarify requirements, challenge assumptions, define pragmatic solutions, and drive projects from design through production launch. • Solve difficult data engineering problems by developing performance-tuned transformations and services using Python, high-performance SQL, and tools such as dbt. • Make sound technical trade-offs at the product level, balancing scalability, reliability, delivery speed, maintainability, and cost. • Author and drive technical designs and RFCs that clearly communicate architecture, alternatives, risks, and operational considerations. • Influence the technical direction of the data products you own and contribute to the team’s broader technical strategy and roadmap. • Set a high quality bar through thoughtful design and code reviews, automated testing, version-controlled schemas, and CI/CD practices for data pipelines. • Lead projects that improve the team’s engineering and operational excellence, including data observability, lineage, incident response, deployment safety, and developer productivity. • Design and operate scalable batch and real-time data solutions using technologies such as Spark, Flink, and Kafka. • Partner with the ML Platform team to deliver clean, reliable, and feature-rich datasets for model training and inference. • Advance data stewardship through strong documentation, discoverability, privacy controls, access management, and governance practices. • Lead postmortems for data incidents, identify systemic improvements, and drive corrective actions to completion. • Break complex projects into clear, deliverable work and help coordinate execution across engineers on the team. • Mentor other engineers, provide actionable technical feedback, and participate in interviewing and hiring. • Participate in the team's on-call rotation, responding to incidents, troubleshooting issues, and contributing to root cause analysis and service reliability improvements. • Respond to and troubleshoot production issues, outages, and critical alerts during assigned on-call shifts. • Escalate incidents as needed and collaborate with team members to restore service. • Document issues and contribute to improvements that reduce recurring incidents. • Be available to provide occasional after-hours, weekend, and holiday support while on call.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience. • 5+ years of relevant industry experience in data engineering, distributed systems, or a related field. • A track record of independently delivering and operating significant production data products end to end. • Experience using AI-assisted development tools to support coding, debugging, testing, documentation, technical exploration, and analysis. • Ability to validate AI-assisted outputs, identifying risks and failure modes, protecting sensitive information, and knowing when deeper manual review is required. • Deep experience building and scaling cloud-based data platforms, preferably using AWS and Snowflake. • Expertise in developing, optimizing, and debugging complex data transformations using Python and high-performance SQL. • Strong understanding of data modeling, ETL/ELT architecture, schema design, and transformation frameworks such as dbt. • Experience designing reliable workflows using orchestration tools such as Airflow. • Hands-on experience with distributed processing or streaming technologies such as Spark, Flink, or Kafka. • Strong technical judgment and experience making trade-offs among performance, reliability, scalability, maintainability, and cost. • Experience establishing or improving data quality, testing, observability, lineage, and operational practices. • Familiarity with modern data governance practices, including cataloging, data classification, PII masking, privacy controls, and access management. • Experience writing technical design documents and communicating complex decisions to technical and non-technical stakeholders. • Experience with technologies such as Iceberg, Debezium, or infrastructure-as-code tools such as Terraform is a plus. • Ability to participate in a shared on-call rotation supporting production systems, including occasional after-hours, weekend, and holiday coverage, with responsibility for responding to critical alerts, coordinating escalations to restore service, and documenting issues to help prevent recurrence.

🏖️ Benefits

• Offers Equity • Offers Bonus