Data Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 7, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Data Engineer Toronto Requisition ID: 257434 Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture. We are looking for a hands‑on Data Engineer with deep expertise in Apache Spark and strong programming skills in Python, Scala, and Java. This role is centered on building high performance, scalable data pipelines and processing large datasets in a distributed environment. You will work primarily on Spark based data processing running on Azure Databricks, developing production grade code that supports enterprise analytics, reporting, and data products. This is an engineering heavy role for someone who enjoys writing clean, efficient code and optimizing distributed workloads. Is this role right for you? In this role, you will: Design, develop, and maintain large‑scale Spark applications using Python, Scala, and/or Java Build and optimize batch and streaming data pipelines in distributed environments Write production‑quality Spark code with strong focus on performance, scalability, and reliability Optimize Spark jobs (partitioning, caching, shuffles, memory tuning, execution plans) Develop reusable Spark frameworks, libraries, and utilities Work with structured and semi‑structured data (Parquet, Delta, CSV, JSON) Collaborate with platform, analytics, and data science teams to support downstream use cases Debug and troubleshoot Spark job failures and performance issues in production Follow best practices for code quality, testing, logging, and documentati

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