A data analyst's code is failing during a large batch upload. After verifying the key, object definitions, syntax, and exception handling, what is the most appropriate next step?

• A. Developing a new code for this process
• B. Asking a senior engineer for peer review of the code
• C. Scheduling a team meeting and getting help from the entire group
• D. Converting the code to a different language

Answer: (B)

When a batch upload fails after you’ve checked the key, object definitions, syntax, and exception handling, bringing in a senior engineer for a peer review is the most effective next step. A peer review provides a fresh set of eyes to spot issues the original author might miss—especially around the logic of how data is transformed and loaded, edge cases, and how errors are handled across large batches. In data pipelines, hidden problems often lie in assumptions about data formats, null values, data type conversions, batch boundaries, and the interaction between steps (loading, validation, and commit/rollback). A senior reviewer can assess these areas, validate that the approach meets standards, and suggest improvements like more robust error handling, better logging, and idempotent behavior to prevent duplicate uploads.

This step is quicker and more focused than assembling the entire team for a meeting, and it’s more productive than starting over with a new language. A language change wouldn’t address the root cause and would introduce new risks and delays. So, the best move is to have a senior engineer review the code to uncover subtle defects and confirm that the batch process is reliable, scalable, and aligned with data quality goals.