Data Engineer

Jobgether; Canada; On-site; CAD 90,000 - 130,000; Full time

Benefits offered by this job

Remote-first

Continuous learning

Global exposure

Conference opportunities

Job summary

Partner Company in Canada seeks a Data Engineer to design scalable data pipelines and automation that illuminate cloud cost visibility and performance insights. You will collaborate with Product, Engineering, and Finance to transform telemetry data into reliable, actionable information.

The role blends hands-on data engineering with opportunities to improve reliability, scalability, and cost efficiency across critical systems while partnering with experienced engineers and business stakeholders

Qualifications

• Strong foundation in data engineering with end-to-end pipeline experience.
• Proficiency in SQL and Python; Scala knowledge is a plus.
• Experience with AWS and GCP, and data warehousing like Snowflake.

Responsibilities

• Design, build, and maintain scalable data pipelines and automation systems.
• Develop robust datasets and data models for visibility, attribution, usage analysis, and performance insights.
• Design ingestion frameworks for large-scale telemetry and cloud usage data.
• Build data tooling for Product, Engineering, and Finance stakeholders to drive cost-aware decisions.
• Optimize data systems for performance, reliability, scalability, and cost.

Skills

SQL

Python

ETL/ELT

Data modeling

Cloud cost

Communication

Tools

dbt

Airflow

Dagster

Terraform

REST APIs

Snowflake

GitHub

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer based in Canada.

This role offers the opportunity to build the data foundations behind cloud cost visibility, attribution, and performance insights. You’ll design scalable pipelines, datasets, and automation systems that help teams understand and optimize cloud spending. Working across Product, Engineering, and Finance, you’ll turn complex usage and telemetry data into reliable, actionable information. The role combines hands‑on data engineering with opportunities to improve performance, reliability, and cost efficiency across critical systems. You’ll work in a collaborative, technology‑focused environment alongside experienced engineers and business stakeholders. It’s an excellent opportunity for a data engineer who enjoys solving complex problems and creating measurable operational impact.

Accountabilities

• Design, build, and maintain scalable data pipelines and automation systems supporting cloud economics and cost intelligence.
• Develop robust datasets and data models that enable cloud cost visibility, attribution, usage analysis, and performance insights.
• Design and implement ingestion frameworks capable of processing large-scale telemetry and cloud usage data.
• Build data tooling that enables Product, Engineering, and Finance stakeholders to make informed, cost-aware decisions.
• Optimize data systems for performance, reliability, scalability, and cost efficiency through query tuning, storage strategies, and compute optimization.
• Partner closely with Engineering and Finance teams to support cloud cost optimization initiatives and usage-based analysis.
• Establish and maintain strong data quality, validation, auditability, and troubleshooting practices across end‑to‑end workflows.
• Contribute to reliable production data operations through effective monitoring, testing, version control, and infrastructure practices.
• Identify opportunities to automate recurring processes and improve the efficiency of data engineering workflows.

Requirements

• Strong foundation in data engineering, with proven experience designing and maintaining end-to-end ETL/ELT pipelines in cloud environments.
• Strong proficiency in SQL and Python; experience with Scala is a plus.
• Hands‑on experience with orchestration and transformation tools such as dbt, Airflow, or Dagster.
• Practical experience with the AWS data ecosystem, including AWS Glue, Amazon Athena, and Amazon Aurora.
• Experience working with Snowflake or another modern cloud data warehouse.
• Strong data modeling and large-scale data processing capabilities.
• Experience with CI/CD practices, GitHub or similar version‑control platforms, and Infrastructure as Code using tools such as Terraform.
• Experience integrating REST APIs into data pipelines and engineering workflows.
• Demonstrated ability to optimize pipelines and data systems for both performance and cost.
• Strong analytical and troubleshooting skills, including experience with data validation, auditing, and resolving production pipeline issues.
• FinOps Foundation certification at Practitioner or Engineer level is a plus.
• Experience with cloud cost optimization, tagging strategies, or cloud cost monitoring is advantageous.
• Experience working across both AWS and GCP is a plus.
• Familiarity with Datadog or comparable monitoring and observability platforms is beneficial.
• Background in data platform engineering or shared infrastructure engineering is an advantage.
• Strong communication and collaboration skills, with the ability to work effectively with technical and business stakeholders.
• Availability to collaborate with teams working primarily across U.S. Eastern or Central time zones.

Benefits

• Competitive long-term total compensation package, including salary and performance-based bonus.
• Remote‑first working environment with a strong emphasis on flexibility.
• Opportunities to work alongside experienced engineers and technology leaders.
• Access to technical and non‑technical training and professional development programs.
• Opportunities for continuous learning and career growth.
• Global exposure through collaboration with international teams and clients.
• Opportunities to attend virtual and in‑person international technology conferences.
• Collaborative environment focused on innovation, knowledge sharing, and technical excellence.