Senior Data Developer, Analytics & Insights

Autodesk•Vancouver, BC

About The Position

Autodesk is looking for a talented and motivated Senior Data Developer to join our Platform Strategy & Emerging Technologies (PSET) organization to develop robust and scalable data pipelines using and improving existing platforms, to support data-driven decision-making across the platform initiatives. The successful candidate will drive performance enhancements, build pipelines, and collaborate with analysts, data scientists, stakeholders and other Data Engineering teams across Autodesk. You will work with cutting edge technologies in the big data space.

Requirements

5+ years of data processing and data engineering experience in a fast-paced, large cloud-based infrastructure (AWS experience required)
Hands-on software development experience in Python
Expert understanding of SQL, dimensional modeling, and analytical data warehouses, such as Snowflake, Presto/Hive
Understanding of Data Engineering best practices for medium to large scale production workloads
Knowledge of big data processing frameworks (e.g. Spark, Hadoop)
Expertise with data pipeline orchestration tools, such as Airflow
Familiar with processing semi-structured file formats such as Json or parquet
Team player with good communication skills
Problem solver with excellent written and interpersonal skills
Bachelor’s degree in computer science, data science, or related fields

Nice To Haves

Experience with Jinja, Shell scripting, DBT, Spark SQL
Experience developing in Cloud platform using serverless technologies such as AWS glue, lambda, EMR and EKS is a plus
Experience with remote development using AWS SDK is a plus
Experience with both ETL and ELT pipelines, including traditional ETL tools (e.g., Airflow, Talend, Informatica) and modern ELT frameworks (e.g., dbt, Snowflake)
Knowledge of AWS IAM roles, permissions, and best practices for least-privilege access
Experience with Terraform for AWS resource provisioning, including remote state management and security best practices
Hands-on experience with AWS networking (VPC, security groups, cross-account permissions)
REST API design and implementation
Familiarity with containers and infrastructure-as-code principles
Experience with automation frameworks - Git, Jenkins, and Terraform
Master’s degree in computer science, data science, or related fields
Experience with AI-native data engineering technologies, including adoption of MCP (Model, Context, Prompt) related context engineering to optimize data flow for AI-driven tasks
Demonstrated ability to structure data for enhanced AI reasoning, enabling more robust and explainable machine learning workflows
Experience in reducing latency for complex agentic workflows, ensuring efficient real-time processing and responsiveness in AI-powered systems

Responsibilities

Design, develop, automate and maintain scalable, robust and reliable ELT/ETL data pipelines that collect, process and transform large volumes of structure and unstructured data from various sources
Maintain and enhance our existing data architecture to ensure smooth and efficient data flow across platforms
Interface with data peers, product managers and cross-functional stakeholders to gather requirements, sequence work, and document technical solutions
Implement best practices for data quality, integrity and governance, including monitoring, validation and auditing processes to ensure reliable and consistent data availability
Contribute to a team culture that values quality, robustness, and scalability while fostering initiatives and innovation by staying up to date with industry trends and new technologies
Apply an AI-native data engineering mindset by using ML-driven validation, anomaly detection, automated schema evolution, automated data lineage and optimizations for pipeline performance and resource efficiency