Data Engineer: Scalable Pipelines & Insights

Rozgoo; Toronto; On-site; CAD 90,000 - 140,000; Full time

Benefits offered by this job

Extended health & dental

Mental health benefits

Family building benefits

Child care benefits

Lyft Health Savings Account

RRSP match

Flexible PTO

Paid parental leave (18 weeks)

Commuter benefits

Job summary

Lyft is looking for a Data Engineer to join the Marketplace – Decision Apps team. You will own and evolve data pipelines powering top-line metrics, pricing and driver analytics, collaborating with Analytics, Data Science and Engineering to model data and deliver reliable insights.

You will write scalable, well-tested code, participate in reviews, and help maintain high availability through on-call rotations while shaping the team’s data engineering roadmap.

Qualifications

• 3+ years of data engineering hands-on experience delivering end-to-end features.
• Experience collaborating with analytics, data science, product and engineering teams.
• Proficient in data validation, analysis and clear insight presentation.
• Strong SQL skills and ability to implement complex business logic.

Responsibilities

• Take ownership of core data pipelines ensuring resilience, performance and quality.
• Evolve data models and schemas to meet business needs.
• Monitor and improve data quality and consistency across systems.
• Develop ETL tooling and perform SQL tuning for performance.
• Contribute to the team's technical roadmap and codebase quality.
• Write clean, scalable, well-tested code and participate in reviews.
• Support on-call rotations to maintain pipeline reliability.

Skills

Python

SQL

Spark

ETL

Airflow

Data modeling

MPP systems

Tools

Airflow

Flyte

Spark

Job description

Lyft is looking for a Data Engineer to join the Marketplace – Decision Apps team. You will own and evolve data pipelines powering top-line metrics, pricing and driver analytics, collaborating with Analytics, Data Science and Engineering to model data and deliver reliable insights.

You will write scalable, well-tested code, participate in reviews, and help maintain high availability through on-call rotations while shaping the team’s data engineering roadmap.