Senior Data Engineer

GuruLink; Toronto; On-site; CAD 100,000 - 140,000; Full time

Job summary

Join a leading company at the forefront of AI-native infrastructure development as a Senior Data Engineer. You'll architect and build scalable data pipelines and real-time systems that drive machine learning and support next-generation workflows. The role offers a chance to work with a top-tier technical team, tackling complex challenges in a collaborative environment, all while enjoying a competitive salary and equity opportunities.

Benefits

Competitive salary

Equity options

Collaborative work environment

Qualifications

• 5–8+ years of experience in data engineering with a strong focus on scalable architecture.
• Proven experience with tools like Kafka or Pulsar for event-based processing.
• Proficiency in Python, Scala, or Java.

Responsibilities

• Design and implement real-time and batch data pipelines to support ML training.
• Build robust streaming ingestion systems using Kafka, Pulsar, or similar frameworks.
• Implement data lineage tooling for traceability, reproducibility, and auditability.

Skills

Data engineering

Event-based data processing

Cloud-native data storage

Data lineage

Python

Scala

Java

Job description

Location: Toronto, Ontario

Our client is building an AI-native infrastructure platform designed with event-driven architecture at its core—and we’re looking for a Senior Data Engineer to design and build the data backbone that supports real-time intelligence, machine learning, and entity-driven workflows. In this role, you’ll be responsible for architecting and implementing scalable data pipelines, real-time ingestion systems, and tools to support ML training, workflow automation, and data lineage across distributed environments.

This role is ideal for an experienced engineer who thrives on building production-ready systems that move fast, scale well, and enable advanced AI capabilities downstream.

What You’ll Do:

- Design and implement real-time and batch data pipelines to support ML training and semantic workflows.

- Build robust streaming ingestion systems using Kafka, Pulsar, or similar event-driven frameworks.

- Work with Delta Lake, Parquet, and other modern storage formats to ensure efficient and queryable datasets.

- Implement data lineage tooling to ensure traceability, reproducibility, and auditability across the platform.

- Collaborate with ML, software, and infrastructure teams to define data models and support experimentation.

- Ensure pipelines are resilient, scalable, and observability-ready from day one.

Special Perks:

Why Join?

Help design the foundational data systems behind an ambitious, AI-native infrastructure platform.

Work with event-driven, real-time architecture and solve hard, high-scale problems.

Collaborate with a deeply technical team across ML, systems, and platform engineering.

Competitive salary, equity, and the opportunity to define how data is built and used across the platform.

Must Have Skills:

What You Bring:

- 5–8+ years of experience in data engineering, with a strong focus on scalable architecture.

- Proven experience with event-based data processing (Kafka, Pulsar, or similar).

- Familiarity with Delta Lake, Parquet, and cloud-native data storage best practices.

- Experience supporting ML pipelines, feature stores, or training data generation workflows.

- Understanding of data lineage, metadata management, and governance tools.

- Proficiency in Python, Scala, or Java, and comfort working in distributed, cloud-based environments.

Nice to Have Skills:

Bonus Points For:

- Experience with real-time analytics, CDC, or streaming joins.

- Familiarity with Kubernetes, Terraform, and data infrastructure as code.

- Prior work in AI platforms, recommendation systems, or data-intensive SaaS products.