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Data Engineer Google Jobs in Toronto, ON (NOW HIRING)

The ideal candidate is a hands-on data engineering professional with strong expertise in ... Experience with AWS, Azure, or Google Cloud Platform * Experience with Kafka, Spark Structured ...

Adastra is seeking a Senior Data Engineer to design, build, and optimize scalable data pipelines ... Experience with AWS, Azure, or Google Cloud Platform * Experience with Kafka, Spark Structured ...

You will build, monitor, and optimize data workflows using tools like Apache Airflow, Google ... Engineering Rigor Strong commitment to automated pipeline testing, continuous integration ...

Data Engineer

Toronto, ON

CA$70K - CA$80K/yr

We'relooking for a Data Engineer with3-5years of hands-on experience to join our team.You'llown the ... Google Ads, GA4, Meta, TikTok, DV360, Campaign Manager 360, LinkedIn Ads, and similar. * Model ...

Google Ads, Meta, or similar platforms) * Advanced degree (Master's or Ph.D.) in Computer Science, Data Engineering, Data Science, or a related quantitative field * Knowledge of database design and ...

Cloud & DevOps * Architect cloud-native data platforms across AWS, Azure, or Google Cloud Platform. * Implement Infrastructure-as-Code using Terraform or similar technologies. * Build and maintain CI ...

We prioritize open-source technologies in our data stack while leveraging Google Cloud Platform ... Partner with DevOps, Analytics Engineering, and other stakeholders to close infrastructure gaps and ...

Cloud Developer - Architecture Location PCS CA~TORONTO Years of Experience 5-7 Years Job Summary We ... cloud solutions, particularly in Google Cloud Platform (GCP). This role requires a deep ...

Cloud Developer - Architecture Location PCS CA~TORONTO Years of Experience 5-7 Years Job Summary We ... cloud solutions, particularly in Google Cloud Platform (GCP). This role requires a deep ...

BI Data Engineer

Markham, ON

CA$100K - CA$115K/yr

BI Data Engineer Full stack engineer for BI / Reporting solution Location: Markham, ON (Hybrid ... Experience with Google charts API * Building and maintaining API layer for data acquisition and ...

Our stackPython, SQL, Bash Google Cloud Platform (GCP) BigQuery and dbt Airflow (Cloud Composer ... Data Engineering Expertise: Strong experience with ETL/ELT development, data modeling, schema ...

The Data Engineer Manager partners closely with data architects, analytics teams, and business ... Hands-on experience with cloud platforms such as Azure, AWS, or Google Cloud * Experience leading ...

Snowflake, Databricks, AWS, Azure, or Google Cloud (GCP) * Experience transforming enterprise-level ... Experience with Data Warehouse design and Data Modelling * Computer Science or Engineering degree ...

... in Google Cloud Platform (GCP) and BigQuery (BQ) to join our dynamic team. The ideal candidate will have a solid background in data engineering, BI solutions, and cloud-based data pipelines, with ...

Experience working with multiple cloud platforms preferably Google Cloud Platform. * Data Modeling ... You will be a key developer on our DBT(data build tool) project, creating modular, tested, and ...

Certifications such as Azure Data Engineer Associate or Google Professional Data Engineer . * Experience with financial compliance standards (e.g., SOX, Basel, IFRS). * Experience working in Agile ...

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Data Engineer Google information

See Toronto, ON salary details

$27.2K

$117.7K

$163.7K

How much do data engineer google jobs pay per year?

As of Aug 7, 2026, the average yearly pay for data engineer google in Toronto, ON is $117,687.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,002.00 and $139,333.00 per year, depending on experience, location, and employer.

What does a data engineer at Google do?

A Data Engineer at Google designs, builds, and manages systems that collect, store, and process large volumes of data. Their responsibilities include creating data pipelines, ensuring data quality, and optimizing data architectures to support analytics and machine learning initiatives. They work closely with data scientists, analysts, and other engineers to ensure that data is accessible, reliable, and efficiently processed for various business needs.

How do data engineers at Google typically collaborate with data scientists and software engineers?

At Google, Data Engineers work closely with both data scientists and software engineers to build robust, scalable data pipelines and infrastructure. Data Engineers are responsible for ensuring that data is clean, accessible, and optimized for analytics, often translating business needs into technical solutions. Regular collaboration happens through cross-functional meetings, design sessions, and code reviews, where Data Engineers provide expertise in data modeling, ETL processes, and system optimization. This collaborative environment promotes innovation, knowledge sharing, and the successful deployment of data-driven products.

What are the key skills and qualifications needed to thrive as a data engineer at Google, and why are they important?

To thrive as a Data Engineer at Google, you need strong programming skills (especially in Python, Java, or Scala), expertise in data modeling, and a solid understanding of distributed systems, typically supported by a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP), BigQuery, SQL, Apache Spark, and relevant data engineering certifications is highly valued. Analytical thinking, effective communication, and problem-solving abilities are crucial soft skills for collaborating across teams and translating business requirements into technical solutions. These skills ensure the reliable design, optimization, and scalability of data systems critical to Google's innovation and decision-making.

Does Google hire data engineers?

Yes, Google hires data engineers to develop and maintain data pipelines, manage large-scale data systems, and support data-driven decision-making. Candidates typically need strong skills in SQL, Python, or Java, along with experience with cloud platforms like Google Cloud Platform (GCP).

What is the difference between Data Engineer Google vs Data Engineer Amazon?

AspectData Engineer GoogleData Engineer Amazon
Required CredentialsBachelor's in CS or related, Google Cloud certifications often preferredBachelor's in CS or related, AWS certifications common
Work EnvironmentGoogle Cloud Platform, large-scale data systems, collaborative teamsAWS cloud services, large data pipelines, cross-functional teams
Employer & Industry UsageGoogle, tech and internet servicesAmazon, e-commerce and cloud services
Search & Comparison IntentHigh overlap in cloud data engineering rolesSimilar roles in cloud data engineering

Both Data Engineer Google and Data Engineer Amazon roles require strong data processing skills, cloud platform knowledge, and relevant certifications. While Google emphasizes Google Cloud Platform expertise, Amazon focuses on AWS. Both roles are integral to their respective companies' data infrastructure, with similar work environments and industry usage, making them common comparison points for data engineering careers in cloud environments.

What are popular job titles related to Data Engineer Google jobs in Toronto, ON? For Data Engineer Google jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Data Engineer Google jobs in Toronto, ON look for? The top searched job categories for Data Engineer Google jobs in Toronto, ON are:
Infographic showing various Data Engineer Google job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $117,687 per year, or $56.6 per hour.

Other

Posted 17 days ago


Job description

Overview

Adastra is seeking a Senior Data Engineer to design, build, and optimize scalable data pipelines and cloud-based data platforms that support advanced analytics, reporting, and AI initiatives. This role is responsible for developing high-performance ETL/ELT solutions using PySpark and modern data engineering technologies, enabling efficient processing of large-scale structured and semi-structured data across enterprise environments.

The ideal candidate is a hands-on data engineering professional with strong expertise in distributed data processing, cloud data platforms, and data pipeline orchestration. This individual will collaborate closely with data architects, data scientists, analysts, and business stakeholders to deliver reliable, scalable, and high-quality data solutions that drive business value and support data-driven decision-making.

Primary Location: Toronto, ON

Work Model: Hybrid 2-Days Onsite

Employment Type: Full-Time or Contractor

Vacancy Status: New

RESPONSIBILITIES

  • Design, develop, and maintain scalable ETL/ELT pipelines using PySpark for batch and near-real-time data processing
  • Develop and optimize Spark jobs using DataFrames, RDDs, and Spark SQL for large-scale data transformations and aggregations
  • Build reusable data ingestion frameworks to support integration from databases, APIs, flat files, and streaming sources
  • Optimize Spark application performance through partitioning, caching, broadcast joins, and cluster resource tuning
  • Collaborate with data scientists, analysts, and business stakeholders to deliver clean, curated datasets for reporting and advanced analytics
  • Implement automated data quality checks, validation rules, and monitoring processes to ensure data reliability
  • Develop and maintain workflow orchestration using Airflow, Oozie, or similar scheduling tools
  • Integrate data pipelines with cloud data lakes and data warehouse platforms such as Snowflake, Redshift, Delta Lake, S3, and HDFS
  • Troubleshoot and resolve pipeline failures, production issues, and distributed processing bottlenecks
  • Maintain technical documentation, version control, and deployment processes for data engineering solutions
  • Participate in code reviews, testing, and CI/CD initiatives to improve solution quality and delivery efficiency
  • Support the continuous improvement of data engineering frameworks, standards, and best practices

QUALIFICATIONS, SKILLS & EXPERIENCE

  • Bachelor's degree in Computer Science, Engineering, Information Technology, Data Analytics, or a related field
  • 5+ years of experience in Data Engineering, ETL/ELT development, or Big Data environments
  • 3+ years of hands-on experience with PySpark and Apache Spark
  • Strong proficiency in Python and SQL
  • Experience building data platforms that support data science and machine learning workloads (Python, Anaconda) 
  • Experience developing Spark applications using DataFrames, RDDs, and Spark SQL
  • Experience building and supporting enterprise-scale data pipelines and distributed data processing solutions
  • Hands-on experience with workflow orchestration tools such as Airflow, Oozie, or equivalent platforms
  • Experience working with cloud data lakes and data warehouse technologies including Snowflake, Redshift, Delta Lake, S3, or HDFS
  • Strong understanding of data modeling, data warehousing, and modern data architecture principles
  • Experience implementing data quality, validation, and monitoring frameworks
  • Familiarity with Git, version control processes, and CI/CD practices
  • Strong analytical, troubleshooting, and performance optimization skills
  • Experience working in Agile delivery environments
  • Excellent communication and collaboration skills

NICE TO HAVE

  • Experience with Databricks and Lakehouse architectures
  • Experience with AWS, Azure, or Google Cloud Platform
  • Experience with Kafka, Spark Structured Streaming, or real-time data processing technologies
  • Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation
  • Experience supporting Machine Learning or AI data platforms
  • Experience in consulting, professional services, or client-facing environments
  • Industry experience within Financial Services, Retail, Healthcare, Telecommunications, Manufacturing, or Public Sector organizations
  • Experience with data governance, metadata management, and data catalog solutions

ABOUT ADASTRA

Adastra is a global leader in AI and data-driven transformation, helping organizations lead with artificial intelligence-responsibly, strategically, and at scale. With over 25 years of experience, Adastra empowers enterprise clients to unlock business value through data innovation, operational excellence, and smart customer engagement.

Trusted by some of the world's most prominent brands, Adastra delivers end-to-end solutions grounded in thoughtful strategy, robust governance, and deep technical expertise. From defining vision to ensuring execution, Adastra guides organizations through every stage of their AI, data and cloud journey-building future-ready capabilities and delivering measurable, lasting impact.

Adastra serves clients across key industries including financial services, automotive, manufacturing, technology, media and telecom (TMT), healthcare, retail, and professional services. The company employs more than 2,000 professionals across North America, Europe, and Asia.

WHAT WE OFFER

  • Opportunity for advancement and career progression
  • Competitive compensation
  • Successful referral program
  • The opportunity to work with one of Canada's 50 Best Managed Companies
  • Satisfaction of working for a reputable company 
  • A flexible, dynamic, and diverse workplace

EQUAL OPPORTUNITY EMPLOYER

In our commitment to promote fair and equitable treatment of all employees and applicants, Adastra Corporation provides equal employment opportunities for all individuals regardless of age, sex, disability, race, ethnic origin, citizenship, creed, sexual orientation, marital status, or any other ground as described in the Ontario Human Rights Code.  In addition, accommodation will be provided during the hiring process. Adastra Corporation's implementation and support of employment initiatives, encourage diversified labour force participation and equal access to opportunities based on merit and performance.

AI Usage - Our hiring process includes the use of AI-enabled tools to screen applications (e.g., keyword matching, qualification ranking). A human recruiter reviews all AI-generated shortlists to make informed hiring decisions.

There has been an increased instance of fraudulent job offers coming from people posing as Adastra HR employees. Please note that Adastra will never request fees as part of our recruiting process and any emails sent to you that are not from '@adastragrp.com' or '@talent.icims.com' are fraudulent. All employment offers are sent via DocuSign and if you receive an employment offer from a suspicious email and not via DocuSign, it is fraudulent.

Contact careers@adastragrp.com to inquire about jobs at Adastra, to report a suspicious request for money or personal information from external websites or suspicious employment offers.

Employment Type: OTHER