•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•Fixing broken queries, incorrect visualizations, outdated screenshots, and mismatched expected outputs.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 modern 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.
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.
•Ability to write high-quality instructional artifacts and technical documentation modeled on industry standards.
•Strong attention to detail with a student-first mindset.
•Experience with statistics or machine learning in production environments is a plus.
•Cloud or data-related certifications (e.g., AWS Data/Analytics, Azure Data Engineer) are a plus.
Before you Apply
If you're interested in joining our contractor pool and contributing to world-class data education, please submit your application with:
•Your resume highlighting relevant data, analytics, or data engineering experience
We look forward to hearing from you!