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Job Requisition ID #
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.
- 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
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!
Salary transparency
Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $107,000 and $157,300. 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.Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging
In-Person Onboarding and Identity Verification
This role may require in-person onboarding and/or in-person ID verification.
Autodesk Toronto, Ontario, CAN Office
661 University Ave, Toronto, ON, Canada, M5G 1M1
