Technology

Data Engineer

🇺🇸 US 🇬🇧 UK 🇦🇺 AU 🇨🇦 CA

Live job postings

3,369

as of 15 Aug 2026

Demand index

1.44%

% of all job postings

Median salary

$120k

$120,000

Salary range

$100k – $140k

$100,000 – $140,000

Trend analysis & outlook

Canada's Data Engineer market is moderately sized with ~4,215 active postings and a median salary of $120,000, reflecting stable demand. The 12-month data shows a gradual decline from ~140,000 postings in mid-2025 to ~130,000 by early 2026, with a sharp drop to ~4,200 by mid-2026, driven by seasonal hiring cycles and a likely shift in job aggregation. The hot index peaked at 99 in February 2026, indicating a brief surge in fast-moving roles, but has since softened to 62 by June. The latest news emphasizes self-study paths and emerging hybrid roles (e.g., Data Security Engineer), which may expand career options without demanding a full pivot. Over the next 6–12 months, expect Data Engineer roles in Canada to stabilize, with demand concentrated in AI-enabling infrastructure, cloud-native pipelines, and security-related data positions. Job seekers should prioritize cloud certifications (AWS/Azure/GCP) and real-time streaming skills to remain competitive.

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Core skills employers ask for

SQLPythonApache SparkCloud Platforms (AWS/GCP/Azure)ETL/ELT Pipeline DesignData ModelingApache AirflowData Warehousing (Snowflake, Redshift)Kafka / Real-time StreamingContainerization (Docker, Kubernetes)CI/CD for Data Pipelinesdbt (Data Build Tool)

AI impact on this career

AI is automating routine data pipeline tasks, such as schema inference, anomaly detection, and basic transformation scripts, making junior-level work less abundant. However, AI tools like Spark MLlib and AutoML platforms are elevating the demand for engineers who can design robust, scalable data architectures that feed AI models. Cloud-native AI services (e.g., AWS SageMaker, GCP Vertex AI) require deep knowledge of data ingestion and feature engineering, shifting Data Engineers closer to AI/ML workflows. The ability to integrate AI-driven recommendations into pipelines and optimize data for LLMs is becoming a premium skill, while manual coding of simple transformations is declining. Overall, AI is not replacing Data Engineers but is reshaping the role towards more strategic, architecture-focused work.