Data Engineer

Lime is on a mission to build a future where transportation is shared, affordable and carbon-free. 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.

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.

Requirements

Reside in Canada; 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, 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.

Nice to have

AWS

Benefits

Equity; Discretionary annual performance bonus