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Added 14 days ago
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Autodesk

Data Engineer

Toronto, ON, Canada

$88,000 to $128,700 CAD / year

Apply on jobs.autodesk.com About employer

Applications open in a new tab.

Job details

Pay: $88,000 to $128,700 CAD / year
Apply by: May 8, 2027
Posting language: English · French
Posted: Nov 9, 2026

Original job description

English section of this bilingual listing.

Job Requisition ID #

26WD101542

Position Overview

Autodesk is looking for a skilled and innovative Senior Data Engineer to join our Compliance team, which builds data-driven systems to detect and address non-compliant usage of Autodesk software. This is a key strategic initiative that leverages large-scale data processing and analytics to drive business impact.

In this role, you will design, build, and optimize scalable data pipelines and distributed processing systems that handle billions of records. You will focus on improving platform reliability, performance, scalability, and operational efficiency while enabling business-critical analytics.

We are looking for an engineer who combines strong expertise in big data technologies with hands-on experience using AI-powered development tools to accelerate engineering workflows, improve code quality, and deliver solutions faster.

The ideal candidate is technically strong, self-driven, comfortable owning complex engineering initiatives, and passionate about leveraging modern technologies and AI-assisted development practices to solve challenging data engineering problems.

Our technology stack includes: Python, SQL, Apache Spark, PySpark, Spark SQL, Apache Airflow, Hive, Presto/Trino, Snowflake, AWS (S3, EMR, Glue), Git, Jenkins, Power BI, Looker, and AI-assisted development tools such as GitHub Copilot, Cursor, ChatGPT, Claude, or equivalent solutions.

Responsibilities

Leverage AI-powered development tools to accelerate coding, debugging, refactoring, test generation, documentation, and troubleshooting of complex data engineering solutions
Integrate AI-assisted development practices into engineering workflows to improve productivity, code quality, and delivery speed
Evaluate and validate AI-generated solutions for correctness, performance, maintainability, and security while following responsible AI practices
Identify opportunities to automate repetitive engineering tasks and improve developer efficiency through AI-powered solutions
Design, develop, and optimize large-scale batch and near-real-time data processing pipelines using Apache Spark, PySpark, and Spark SQL
Build scalable and efficient ETL/ELT pipelines capable of processing billions of records across distributed data platforms
Optimize distributed processing workloads through partitioning, efficient joins, query tuning, resource management, and performance analysis
Develop and maintain data processing solutions using technologies such as Hive, Snowflake, AWS S3, EMR, and Glue
Build and maintain production-grade data workflows using Apache Airflow, including DAG development, scheduling, dependency management, retries, and failure recovery
Implement data quality checks, pipeline monitoring, observability, and alerting to ensure reliable data processing
Design reusable, modular, and fault-tolerant pipelines supporting incremental processing, backfills, and operational scalability
Troubleshoot production issues and continuously improve pipeline reliability, performance, and cost efficiency
Develop and maintain scalable, cloud-native data infrastructure on AWS
Automate infrastructure provisioning, deployments, and operational processes using Infrastructure as Code and CI/CD practices
Implement automated testing, version control, code reviews, and software engineering best practices
Own the end-to-end delivery of data engineering solutions, from technical design and implementation to deployment and production support
Partner with product managers, data scientists, analysts, and business stakeholders to translate business requirements into scalable technical solutions
Design efficient data models and curated datasets to support compliance detection, product usage analytics, and business reporting
Define and promote data engineering standards, reusable frameworks, and architectural best practices
Communicate technical designs, trade-offs, and engineering outcomes effectively to stakeholders

Minimum Qualifications

4–7 years of relevant experience in data engineering, big data systems, or software engineering, building and operating production-grade data platforms
3+ years of hands-on experience with Apache Spark, including PySpark, Spark SQL, and DataFrame APIs
3+ years of strong programming experience in Python and SQL, with demonstrated ability to write clean, modular, maintainable, and testable code
Strong understanding of distributed computing fundamentals, including parallel processing, partitioning, shuffling, data skew, fault tolerance, and Spark performance tuning
Hands-on experience with Apache Airflow for building, scheduling, and orchestrating complex data pipelines
Strong knowledge of ETL/ELT design patterns, data modeling, SQL optimization, and large-scale data processing architectures
Practical experience using AI-powered coding tools such as GitHub Copilot, Cursor, ChatGPT, Claude, or equivalent solutions in day-to-day software or data engineering development
Ability to effectively use AI tools for code generation, debugging, refactoring, testing, and accelerating engineering problem-solving
Understanding of AI-assisted development best practices, including prompt formulation, code validation, secure usage, and identifying limitations of AI-generated solutions
Experience with AWS data services such as S3, EMR, Glue, or equivalent cloud-based data processing platforms
Working knowledge of analytical data platforms such as Snowflake, Hive, or Presto/Trino
Experience with Git, CI/CD pipelines, and automation tools such as Jenkins
Familiarity with data quality frameworks, pipeline observability, production monitoring, and troubleshooting
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Preferred Qualifications

Experience with modern data lake or lakehouse architectures, including Apache Iceberg, Delta Lake, or Apache Hudi
Familiarity with streaming data processing technologies such as Apache Kafka or Spark Structured Streaming
Experience with Infrastructure as Code tools such as Terraform or AWS CloudFormation
Experience developing reusable data engineering frameworks, libraries, or internal developer productivity tools
Familiarity with LLM APIs, AI agents, or AI-powered workflow automation applied to software or data engineering
Experience with BI and analytics platforms such as Power BI or Looker
Demonstrated experience improving engineering efficiency, pipeline performance, or cloud infrastructure costs through automation and optimization

Learn More / En savoir plus

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

À propos d’Autodesk

Belonging / appurtenance
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

This role may require in-person onboarding and/or in-person ID verification.

Read the full bilingual original

Job Requisition ID #

26WD101542

Position Overview

Responsibilities

Leverage AI-powered development tools to accelerate coding, debugging, refactoring, test generation, documentation, and troubleshooting of complex data engineering solutions
Integrate AI-assisted development practices into engineering workflows to improve productivity, code quality, and delivery speed
Evaluate and validate AI-generated solutions for correctness, performance, maintainability, and security while following responsible AI practices
Identify opportunities to automate repetitive engineering tasks and improve developer efficiency through AI-powered solutions
Design, develop, and optimize large-scale batch and near-real-time data processing pipelines using Apache Spark, PySpark, and Spark SQL
Build scalable and efficient ETL/ELT pipelines capable of processing billions of records across distributed data platforms
Optimize distributed processing workloads through partitioning, efficient joins, query tuning, resource management, and performance analysis
Develop and maintain data processing solutions using technologies such as Hive, Snowflake, AWS S3, EMR, and Glue
Build and maintain production-grade data workflows using Apache Airflow, including DAG development, scheduling, dependency management, retries, and failure recovery
Implement data quality checks, pipeline monitoring, observability, and alerting to ensure reliable data processing
Design reusable, modular, and fault-tolerant pipelines supporting incremental processing, backfills, and operational scalability
Troubleshoot production issues and continuously improve pipeline reliability, performance, and cost efficiency
Develop and maintain scalable, cloud-native data infrastructure on AWS
Automate infrastructure provisioning, deployments, and operational processes using Infrastructure as Code and CI/CD practices
Implement automated testing, version control, code reviews, and software engineering best practices
Own the end-to-end delivery of data engineering solutions, from technical design and implementation to deployment and production support
Partner with product managers, data scientists, analysts, and business stakeholders to translate business requirements into scalable technical solutions
Design efficient data models and curated datasets to support compliance detection, product usage analytics, and business reporting
Define and promote data engineering standards, reusable frameworks, and architectural best practices
Communicate technical designs, trade-offs, and engineering outcomes effectively to stakeholders

Minimum Qualifications

4–7 years of relevant experience in data engineering, big data systems, or software engineering, building and operating production-grade data platforms
3+ years of hands-on experience with Apache Spark, including PySpark, Spark SQL, and DataFrame APIs
3+ years of strong programming experience in Python and SQL, with demonstrated ability to write clean, modular, maintainable, and testable code
Strong understanding of distributed computing fundamentals, including parallel processing, partitioning, shuffling, data skew, fault tolerance, and Spark performance tuning
Hands-on experience with Apache Airflow for building, scheduling, and orchestrating complex data pipelines
Strong knowledge of ETL/ELT design patterns, data modeling, SQL optimization, and large-scale data processing architectures
Practical experience using AI-powered coding tools such as GitHub Copilot, Cursor, ChatGPT, Claude, or equivalent solutions in day-to-day software or data engineering development
Ability to effectively use AI tools for code generation, debugging, refactoring, testing, and accelerating engineering problem-solving
Understanding of AI-assisted development best practices, including prompt formulation, code validation, secure usage, and identifying limitations of AI-generated solutions
Experience with AWS data services such as S3, EMR, Glue, or equivalent cloud-based data processing platforms
Working knowledge of analytical data platforms such as Snowflake, Hive, or Presto/Trino
Experience with Git, CI/CD pipelines, and automation tools such as Jenkins
Familiarity with data quality frameworks, pipeline observability, production monitoring, and troubleshooting
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Preferred Qualifications

Experience with modern data lake or lakehouse architectures, including Apache Iceberg, Delta Lake, or Apache Hudi
Familiarity with streaming data processing technologies such as Apache Kafka or Spark Structured Streaming
Experience with Infrastructure as Code tools such as Terraform or AWS CloudFormation
Experience developing reusable data engineering frameworks, libraries, or internal developer productivity tools
Familiarity with LLM APIs, AI agents, or AI-powered workflow automation applied to software or data engineering
Experience with BI and analytics platforms such as Power BI or Looker
Demonstrated experience improving engineering efficiency, pipeline performance, or cloud infrastructure costs through automation and optimization

Learn More / En savoir plus

About Autodesk

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

À propos d’Autodesk

Bienvenue chez Autodesk ! Nos logiciels créent chaque jour des choses extraordinaires : des bâtiments les plus écologiques aux voitures les plus propres, en passant par les usines les plus intelligentes et les films à succès. Nous aidons les innovateurs à concrétiser leurs idées, transformant non seulement la façon dont les choses sont fabriquées, mais aussi ce qui peut l’être.

Nous sommes très fiers de notre culture chez Autodesk ; elle est au cœur de tout ce que nous faisons. Notre culture guide notre façon de travailler et de nous comporter les uns envers les autres, influence nos interactions avec nos clients et nos partenaires, et définit notre image au monde.

En tant qu’Autodeskien, vous pouvez accomplir un travail significatif qui contribue à bâtir un monde meilleur, conçu et réalisé pour tous. Prêt à façonner le monde et votre avenir ? Rejoignez-nous !

Salary transparency / Transparence Salariale

Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $88,000 and $128,700 Annual. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package. Le salaire est l'un des éléments du programme de rémunération concurrentiel d'Autodesk. Pour les postes basés au Canada, nous offrons un salaire de base entre $88,000 et $128,700 annuel. Le salaire est déterminé selon l'expérience professionnelle et l'emplacement du candidat(e), et peut dépasser cette fourchette. En plus du salaire de base, notre programme de rémunération peut inclure une prime annuelle, des commissions pour les postes de ventes, des attributions d'actions et un ensemble complet d'avantages sociaux.

Belonging / appurtenance
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

Nous sommes fiers de cultiver une culture d’appartenance où chacun peut s’épanouir. Pour en savoir plus, cliquez ici: https://www.autodesk.com/company/global-belonging

In-Person Onboarding and Identity Verification

Intégration en présentiel et vérification d’identité

This role may require in-person onboarding and/or in-person ID verification.

Ce poste peut nécessiter une intégration et/ou une vérification d'identité en présentiel.

Apply on jobs.autodesk.com About employer

More about this job

Responsibilities

The Senior Data Engineer at Autodesk will design and maintain large data pipelines that process billions of records, keep them reliable and fast, and use AI tools to speed up coding and testing. The role also works with product, analytics and business teams to turn requirements into data solutions.

Requirements

Candidates need 4-7 years of data engineering experience, including 3+ years with Apache Spark and 3+ years of Python and SQL. They also need hands-on Airflow experience, AWS data services and familiarity with AI-powered coding tools.

Skills

Apache Spark
PySpark
Spark SQL
DataFrame APIs
Apache Airflow
Python
SQL
Hive
Presto/Trino
Snowflake
AWS S3
AWS EMR
AWS Glue
Git
Jenkins
CI/CD
GitHub Copilot
Cursor
ChatGPT
Claude
Power BI
Looker
Distributed computing
Data modeling
ETL/ELT design

Benefits

Annual cash bonus
Stock grants

Keywords

Senior Data Engineer
Apache Spark
PySpark
Spark SQL
Apache Airflow
Python
SQL
Hive
Presto
Trino
Snowflake
AWS
S3
EMR
Glue
ETL
ELT
data pipelines
AI-assisted development
GitHub Copilot

Seniority

Senior

Perks

Performance bonus
Stock or equity

Tools

Apache Spark
PySpark
Apache Airflow
Snowflake
AWS S3
AWS EMR
AWS Glue
Hive
Presto/Trino
Git

Languages

English
French

Fields of study

Computer Science
Engineering

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Work requirements

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