Senior Data Engineer

Job ID26WD101542Job Requisition ID #26WD101542Position OverviewAutodesk 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.ResponsibilitiesLeverage AI-powered development tools to accelerate coding, debugging, refactoring, test generation, documentation, and troubleshooting of complex data engineering solutionsIntegrate AI-assisted development practices into engineering workflows to improve productivity, code quality, and delivery speedEvaluate and validate AI-generated solutions for correctness, performance, maintainability, and security while following responsible AI practicesIdentify opportunities to automate repetitive engineering tasks and improve developer efficiency through AI-powered solutionsDesign, develop, and optimize large-scale batch and near-real-time data processing pipelines using Apache Spark, PySpark, and Spark SQLBuild scalable and efficient ETL/ELT pipelines capable of processing billions of records across distributed data platformsOptimize distributed processing workloads through partitioning, efficient joins, query tuning, resource management, and performance analysisDevelop and maintain data processing solutions using technologies such as Hive, Snowflake, AWS S3, EMR, and GlueBuild and maintain production-grade data workflows using Apache Airflow, including DAG development, scheduling, dependency management, retries, and failure recoveryImplement data quality checks, pipeline monitoring, observability, and alerting to ensure reliable data processingDesign reusable, modular, and fault-tolerant pipelines supporting incremental processing, backfills, and operational scalabilityTroubleshoot production issues and continuously improve pipeline reliability, performance, and cost efficiencyDevelop and maintain scalable, cloud-native data infrastructure on AWSAutomate infrastructure provisioning, deployments, and operational processes using Infrastructure as Code and CI/CD practicesImplement automated testing, version control, code reviews, and software engineering best practicesOwn the end-to-end delivery of data engineering solutions, from technical design and implementation to deployment and production supportPartner with product managers, data scientists, analysts, and business stakeholders to translate business requirements into scalable technical solutionsDesign efficient data models and curated datasets to support compliance detection, product usage analytics, and business reportingDefine and promote data engineering standards, reusable frameworks, and architectural best practicesCommunicate technical designs, trade-offs, and engineering outcomes effectively to stakeholdersMinimum Qualifications4–7 years of relevant experience in data engineering, big data systems, or software engineering, building and operating production-grade data platforms3+ years of hands-on experience with Apache Spark, including PySpark, Spark SQL, and DataFrame APIs3+ years of strong programming experience in Python and SQL, with demonstrated ability to write clean, modular, maintainable, and testable codeStrong understanding of distributed computing fundamentals, including parallel processing, partitioning, shuffling, data skew, fault tolerance, and Spark performance tuningHands-on experience with Apache Airflow for building, scheduling, and orchestrating complex data pipelinesStrong knowledge of ETL/ELT design patterns, data modeling, SQL optimization, and large-scale data processing architecturesPractical experience using AI-powered coding tools such as GitHub Copilot, Cursor, ChatGPT, Claude, or equivalent solutions in day-to-day software or data engineering developmentAbility to effectively use AI tools for code generation, debugging, refactoring, testing, and accelerating engineering problem-solvingUnderstanding of AI-assisted development best practices, including prompt formulation, code validation, secure usage, and identifying limitations of AI-generated solutionsExperience with AWS data services such as S3, EMR, Glue, or equivalent cloud-based data processing platformsWorking knowledge of analytical data platforms such as Snowflake, Hive, or Presto/TrinoExperience with Git, CI/CD pipelines, and automation tools such as JenkinsFamiliarity with data quality frameworks, pipeline observability, production monitoring, and troubleshootingBachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experiencePreferred QualificationsExperience with modern data lake or lakehouse architectures, including Apache Iceberg, Delta Lake, or Apache HudiFamiliarity with streaming data processing technologies such as Apache Kafka or Spark Structured StreamingExperience with Infrastructure as Code tools such as Terraform or AWS CloudFormationExperience developing reusable data engineering frameworks, libraries, or internal developer productivity toolsFamiliarity with LLM APIs, AI agents, or AI-powered workflow automation applied to software or data engineeringExperience with BI and analytics platforms such as Power BI or LookerDemonstrated experience improving engineering efficiency, pipeline performance, or cloud infrastructure costs through automation and optimizationLearn MoreAbout AutodeskWelcome 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 transparencySalary 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.BelongingWe take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: Onboarding and Identity VerificationThis role may require in-person onboarding and/or in-person ID verification.#J-18808-Ljbffr