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Databricks Jobs in Ontario (NOW HIRING)

Flexstaf IT

Data Science Developer - Senio

Toronto, ON · On-site

MUST HAVES Must Haves: * 5+ years experience Azure environment * 5+ years experience Data engineering with ADF and Databricks * 5+ years experience Programming experience with Python, SQL Location: 3 ...

ShyftLabs

Data Engineer

Toronto, ON · Hybrid

CA$100K - CA$140K/yr

Design, implement, and optimize big data pipelines in Databricks. * Develop scalable ETL workflows to process large datasets. * Leverage Apache Spark for distributed data processing and real-time ...

Royal Bank of Canada

Staff Data Engineer

Toronto, ON · On-site

Manage Azure Databricks clusters, compute resources, and workspace optimization for scalable data processing * Optimize Power BI reporting infrastructure, semantic models, and dashboard performance ...

Bits In Glass

Sr. Data Engineer

Toronto, ON · On-site

Hands-on experience with data lake and warehouse technologies (e.g., Databricks, Snowflake, Redshift, Synapse). * Deep understanding of data modeling, data integration, and ETL/ELT design.

Cineplex

Sr. Data Engineer

Toronto, ON · Hybrid

Own and support data integrations between Databricks, Braze, and other customer engagement platforms. * Design reliable and secure pipelines that enable audience activation across marketing ...

Scotiabank

Data Engineer

Toronto, ON · On-site

This role will focus on modern data engineering practices, including Azure, Databricks, Unity Catalog, ETL/ELT pipeline development, dbt-based transformation, CI/CD automation, and data platform ...

DoiT

Senior Technical Content Writer

Ottawa, ON · Remote

Research, outline, draft, and revise comprehensive guides on Databricks and Snowflake cost management topics (compute optimization, job cluster configuration, Unity Catalog economics, photon ...

DoiT

Senior Technical Content Writer

Toronto, ON · Remote

Research, outline, draft, and revise comprehensive guides on Databricks and Snowflake cost management topics (compute optimization, job cluster configuration, Unity Catalog economics, photon ...

Menlo Ventures

Director, Field Engineering

Toronto, ON · On-site

$250/hr

To embody and promote Databricks' customer-obsessed, teamwork and diverse culture * Support increased return on investment of Solutions Architect involvement in sales cycles * Create trust-based ...

Vangard, Inc.

Sr Data Engineer, Specialist

Toronto, ON · On-site

CA$90K - CA$140K/yr

We are looking for a Sr Data Engineer with in-depth expertise in AWS, Databricks, and modern data architecture and data modeling to help build the next generation of our data foundation , including ...

BDO Canada

Senior Machine Learning Engineer

Ottawa, ON

CA$84K - CA$128K/yr

Design and implement end-to-end MLOps pipelines using Databricks, MLflow, and related tools. * Build and manage scalable data and feature engineering pipelines in Databricks. * Automate model ...

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All JobsDatabricks JobsDatabricks Jobs in Ontario

Databricks information

See Ontario salary details

$85.5K

$136.5K

$168K

How much do databricks jobs pay per year?

As of Sep 8, 2026, the average yearly pay for databricks in Ontario is $136,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,500.00 and $153,500.00 per year, depending on experience, location, and employer.

What is a Databricks?

A Databricks job is a way to run an automated workload, such as a data pipeline, machine learning model training, or ETL task, on the Databricks platform. Jobs can be scheduled, triggered manually, or run as part of a workflow. They support different task types, including notebooks, Python scripts, JARs, and SQL queries. Databricks jobs also allow for dependency management and orchestration across multiple tasks within a workflow.

What are the typical daily responsibilities of someone working in a Databricks role?

Professionals in Databricks roles typically spend their days developing and maintaining data pipelines, analyzing large datasets, and collaborating with business stakeholders to translate requirements into scalable solutions. They often use tools such as Apache Spark and cloud platforms to design and optimize workflows, while troubleshooting data quality or performance issues that arise. Regular teamwork with data engineers, analysts, and software developers is common, as is participating in sprint planning or code review sessions. Overall, the role combines hands-on technical work with ongoing collaboration to ensure data-driven insights and infrastructure reliability.

What are the key skills and qualifications needed to thrive in the Databricks position, and why are they important?

To thrive in a Databricks role, you need strong programming skills in languages such as Python or Scala, a deep understanding of data engineering or data science principles, and typically a relevant degree in computer science or a related field. Experience with Apache Spark, cloud platforms like Azure or AWS, and Databricks-specific certifications are often highly valued. Exceptional problem-solving, communication, and collaboration skills help professionals excel within multidisciplinary data teams. These capabilities are crucial for successfully designing, developing, and optimizing large-scale data solutions in a fast-evolving analytics environment.

Are Databricks in high demand?

Databricks-related roles, such as data engineers and data scientists, are in high demand due to the platform's widespread adoption for big data analytics and machine learning. Skills in Spark, cloud environments, and data pipeline development increase employability in this field.

Does Databricks hire remote employees?

Databricks offers remote work opportunities for certain roles, especially those related to software engineering, data science, and cloud infrastructure. The availability of remote positions depends on the specific job and team requirements, and candidates should review individual job postings for location details.

Is Databricks a good company to work for?

As a company, Databricks is known for its focus on data analytics and cloud-based platforms, offering roles that involve working with tools like Apache Spark and machine learning. Employee reviews often cite a collaborative environment and opportunities for skill development, though experiences can vary by role and location.

What are jobs in Databricks?

Jobs in Databricks refer to roles that involve developing, managing, and optimizing data workflows using the Databricks platform, which is built on Apache Spark. These positions often require skills in data engineering, data science, or machine learning, and may involve working with cloud environments, SQL, and programming languages like Python or Scala.

What are the most commonly searched types of Databricks jobs in Ontario?

The most popular types of Databricks jobs in Ontario are:

What are popular job titles related to Databricks jobs in Ontario?

For Databricks jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Databricks jobs in Ontario look for?

The top searched job categories for Databricks jobs in Ontario are:

What cities in Ontario are hiring for Databricks jobs?

Cities in Ontario with the most Databricks job openings:

Databricks jobs near you
Infographic showing various Databricks job openings in Ontario as of August 2026, with employment types broken down into 85% Full Time, 10% Part Time, and 5% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $136,456 per year, or $65.6 per hour.

Senior Lead Data & AI Engineer

Vangard, Inc.

Toronto, ON • On-site

Full-time

Re-posted 27 days ago

Job description

Join us in a high-impact, high-visibility role where you will pioneer world-class Data & AI engineering solutions, building the next generation of intelligent, agent-driven systems that power real-time business decisions.
We are seeking an expert Lead Data & AI Engineer with 9 to 12+ years of experience to architect and deploy AI agents into business workflows, focusing on Databricks and AWS environments. You will lead data engineering, AI agent orchestration, and scalable, production-grade AI architectures.

Key Responsibilities:

  • Architect and build reusable, metadata-driven data and AI engineering frameworks that standardize ingestion, transformation, feature engineering, and AI workflow deployment. Leverage Databricks, lakehouse architecture, declarative pipelines, and cloud-native services to enable scalable, governed, and reusable data products across the organization.

  • Architect and deploy Delta Live Tables and Lakeflow jobs on Databricks to automate data processing, AI pipelines, and agent data refresh cycles.

  • Leverage Databricks Workflows and Job Orchestration to schedule and monitor AI agent deployments across multiple business workflows.

  • Integrate Lakeflow for real-time data stream processing, ensuring AI agents are updated and responsive to live data.

  • Ensure seamless orchestration between AI models and data pipelines, using event-driven architectures for real-time inference and deployment.

  • Implement and orchestrate AI agents using frameworks such as Agentic systems, AgentOps tooling, and solutions like Agents on Databricks (Agent-bricks).

  • Hands-on experience deploying AI agents using RAG, Graph RAG, MCP-enabled integrations, and agent orchestration frameworks such as AgentOps, AgentBricks, LangGraph, or cloud-native orchestration services.

  • Manage AI agent lifecycles, monitoring, and scaling using tools like SageMaker, Bedrock, or AI orchestration frameworks on AWS.

  • Ensure robust data governance, metadata management, and AI observability through Unity Catalog, AWS Glue, or custom metadata layers.

  • Design for scalability and modularity, ensuring AI agents are reusable across multiple business processes.

Qualifications:

  • 9-12+ years in data engineering, specializing in AI deployment within cloud ecosystems (Databricks, AWS).

  • Hands-on experience deploying AI agents using frameworks like RAG, graph RAG, and orchestrating agents (AgentOps, Agent-bricks, etc.).

  • Proficient in AWS AI/ML services (SageMaker, Bedrock) and orchestration tools (MWAA, Step Functions).

  • Strong knowledge of lakehouse architecture, Unity Catalog, and data modeling best practices.

  • Deep experience in data orchestration, monitoring, and scalable AI-driven workflows.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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