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About Udacity

Udacity is now an Accenture company, and exciting things are happening! 🚀 We are on a mission of forging futures in tech through radical talent transformation in digital technologies. We offer a unique and immersive online learning platform, powering corporate technical training in fields such as Artificial Intelligence, Machine Learning, Data Science, Autonomous Systems, Cloud Computing and more. Our rapidly growing global organization is revolutionizing how the enterprise market bridges the talent shortage and skills gaps during its digital transformation journey.

Udacity is one of the most exciting companies in Silicon Valley, and is disrupting the way we learn! Named as one of CNBC's Top 50 Disruptors, Udacity is becoming the preferred route for people to obtain the skills they need to get the jobs they want. 

 

About the Job

Udacity is a pioneer in online technical education, offering high-quality courses across a wide range of disciplines. Our catalog includes short and long programs, Nanodegrees (bundled courses), and content tailored to multiple skill levels, foundational, beginner, intermediate, and advanced, as well as business leadership audiences.

To ensure our content remains current, impactful, and industry-aligned, we continuously review and update our courses. We take a data-driven approach to evaluating content quality and identifying outdated material. Key performance metrics, such as student satisfaction, lesson ratings, and page-level feedback, help us determine whether a course requires maintenance. Throughout the year, various courses are kept under active maintenance to ensure they receive timely updates. To do this effectively, we regularly collaborate with expert contractors who help update the course content.

As new needs arise, we contact qualified candidates within our contractor pool to share project details, scope, and timelines. Contractors work closely with a Udacity team member who provides tooling, guidance, and logistical support. In most cases, contractors operate as individual contributors, though they may collaborate with other teams, such as Content Developers, Program Managers, and Learning Architects, to define scope, set priorities, and gather necessary information about the content under maintenance.

 

If you thrive on challenges, want to make an impact, and are interested in joining our contractor community, we encourage you to read on and apply.

About the School of Data Science

We’re building a contractor pool of data professionals to support our School of Data Science. The School of Data Science currently offers courses and Nanodegrees across the end-to-end data lifecycle, including (but not limited to):

• Data Literacy and Data Fluency
• Data Analytics and Business Analytics (SQL, Spreadsheets, Power BI, Tableau)
• Data Visualization and Data Storytelling
• Statistics, Probability, and Experimental Design
• Data Science and Machine Learning Fundamentals (Python, R, ML pipelines)
• Data Engineering and Streaming (Airflow, Kafka, Spark, Data Lakes/Lakehouses, Data Warehouses)
• Data Architecture, Data Governance, and Data Privacy
• Cloud Data Solutions on AWS and Azure (e.g., Redshift, Synapse, Databricks, S3/ADLS, etc.)

Our contractor pool helps ensure this catalog stays technically accurate, pedagogically sound, and aligned with industry practices in data, analytics, and AI.

Understanding Our Learning Infrastructure

To effectively maintain and update our data courses, you'll need to understand how students interact with our content. Our courses use two key technologies: 

Udacity Workspaces
For practitioner content, we provide in-classroom workspaces so students don’t need to install or purchase any tools or set up environments locally. These workspaces are Docker containers running in Kubernetes, and students access them directly in the classroom page through their browser. For the School of Data Science, common workspace types include:

• Jupyter Notebooks: for Python- and R-based data analysis, statistics, and machine learning
• SQL Workspaces: browser-based SQL UIs against managed databases
• VS Code Workspaces: for more complex data engineering, data science, and software-for-data workflows

These workspaces need continuous updates and patching, and the exercises/project starter code must be updated to remain compatible with the updated workspace (e.g., Python libraries and data engineering toolchains).

Udacity Cloud Labs
We also provide temporary access to various cloud services via Cloud Labs. For the School of Data Science, these are primarily AWS and Azure labs that power data engineering, data architecture, and streaming exercises and projects. Cloud Labs are federated accounts that allow students to use the AWS or Azure consoles using temporary credentials. These cloud labs are pre-configured with RBAC and policies. In some cases, we pre-create several data resources, such as data warehouses, data lakes, Kafka clusters, or compute environments, via Infrastructure as Code to provision the resources required for an exercise or project.

Responsibilities

1. Analyze course performance metrics (lesson ratings, pass rates, and qualitative feedback) to identify content requiring updates.
2. Review student feedback at scale to prioritize actionable improvements
3. Bug-fixes: Address student-reported issues by updating or enhancing existing course materials. This includes:  
1. Updating classroom instructions to reflect the latest data tools, cloud UI changes (AWS, Azure), and library behavior.
2. Debugging and updating code in Jupyter notebooks, VS Code workspaces, and SQL exercises (e.g., Python, SQL, and occasionally R).
3. Fixing broken queries, incorrect visualizations, outdated screenshots, and mismatched expected outputs. 
4. Enhancements: Update course content to align with the latest tools and technologies across the data stack. This may include:
1. Updating text, screenshots, diagrams, and examples to reflect current best practices.
2. Refreshing tutorials, exercises, and projects to use modern data workflows, APIs, or libraries (e.g., newer versions of pandas, scikit-learn, PySpark, or BI tools).
3. Improving project rubrics, starter code, and data sets for clarity and robustness.
5. Workspace and environment updates
1. Update Udacity Workspaces using self-service Studio (in-house tool).
2. Install and validate updated Python and R packages in existing workspaces.
3. Update exercises and project starter code to support newer programming environments (e.g., upgrading older Python versions, or updating SQL dialect usage to match the current engine).
6. Cloud Lab validation and troubleshooting
1. Test the Cloud Labs used for data engineering and architecture content.
2. Verify that the necessary cloud services (e.g., data warehouses, storage accounts, streaming services, compute) required for all exercises in a course are enabled and properly configured in the cloud labs.
3. Troubleshoot student access issues and permission-related problems in federated cloud accounts (AWS and Azure), particularly around data access, IAM/RBAC, and resource usage.

Required Skill Set

A qualified candidate will have: 

• Strong understanding of core data skills, including:
• Data wrangling, exploratory data analysis, and basic statistics.
• Writing and optimizing SQL queries.
• Building and interpreting data visualizations and dashboards.
• Hands-on experience with Python for data (pandas, NumPy, scikit-learn, visualization libraries, basic ML) and/or R for data analysis.
• Familiarity with at least one BI or visualization tool, such as Power BI or Tableau.
• Familiarity with foundational cloud data services on AWS or Azure (e.g., S3/ADLS, data warehouses, basic compute, and storage patterns).
• Familiarity with containerization technologies (Docker) and notebook-based workflows (Jupyter).
• At least 2 years of professional experience in a data-related role (e.g., Data Analyst, Data Scientist, Data Engineer, BI Developer).
• Experience working with version control systems (Git/GitHub).
• Ability to debug and update Python-based and SQL-based exercises and projects.
• Strong troubleshooting skills to resolve student-reported issues efficiently (e.g., environment mismatches, package conflicts, SQL errors, visualization failures).
• Excellent written communication skills for documenting changes and providing clear, step-by-step instructions.
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