Senior Data Engineer

## Position 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.## 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 too