Data Engineer

Lime•,

5d•CA$121,000 - CA$147,000•Remote

About The Position

As a global leader in micromobility, Lime is on a mission to build a future where transportation is shared, affordable and carbon-free. A Time Magazine 100 Most Influential Company, Lime has powered more than one billion rides in close to 30 countries across five continents, spurring a new generation of clean alternatives to car ownership. Learn more at li.me. The Data Engineering team is responsible for architecting, building, and scaling the foundational data infrastructure that powers our global fleet’s analytics, machine learning models, and real-time business intelligence. We are looking for a Data Engineer to join us in solving complex data scaling, quality, and governance challenges. This is a remote position with a requirement for candidates to reside in Canada to maintain effective collaboration across teams.

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).

Nice To Haves

Experience with Iceberg, Debezium, or Infrastructure-as-Code tools like Terraform for managing data infrastructure.

Responsibilities

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.