Job Summary

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.)

Understanding Our Learning Infrastructure

To effectively maintain and update our data courses, youll 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

- Analyze course performance metrics (lesson ratings, pass rates, and qualitative feedback) to identify content requiring updates.
- Review student feedback at scale to prioritize actionable improvements
- Bug-fixes: Address student-reported issues by updating or enhancing existing course materials. This includes:
- Updating classroom instructions to reflect the latest data tools, cloud UI changes (AWS, Azure), and library behavior.
- Debugging and updating code in Jupyter notebooks, VS Code workspaces, and SQL exercises (e.g., Python, SQL, and occasionally R).
- Fixing broken queries, incorrect visualizations,



outdated screenshots, and mismatched expected outputs.

- Enhancements: Update course content to align with the latest tools and technologies across the data stack. This may include:

- Updating text, screenshots, diagrams, and examples to reflect current best practices.
- Refreshing tutorials, exercises, and projects to use contemporary data workflows, APIs, or libraries (e.g., newer versions of pandas, scikit-learn, PySpark, or BI tools).
- Improving project rubrics, starter code, and data sets for clarity and robustness.

- Workspace and environment updates

- Update Udacity Workspaces using self-service Studio (in-house tool).
- Install and validate updated Python and R packages in existing workspaces.
- 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).

- Cloud Lab validation and troubleshooting

- Test the Cloud Labs used for data engineering and architecture content.
- 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.
- Troubleshoot student access issues and permission-related problems in federated cloud accounts (AWS and Azure), particularly around data access, IAM/RBAC, and resource usage.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 On-Call Maintenance Specialist, Data Science - Contract Role (India)
🏢 Udacity
📍 India