The Pros and Cons of Vibe Coding with AI in Data Engineering
By Anton Schaffer – Principal Data Engineer. AI-assisted development has become a game changer across many fields, but in data engineering it’s particularly disruptive. After all, data engineers spend half their lives untangling JSON, arguing with SQL about GROUP BYs, and trying to remember if it’s left join or left outer join (spoiler: it’s both). Enter “vibe coding”, where instead of meticulously planning pipelines, you simply tell the AI “Hey, compare these two files for me” and see what it dreams up. It’s like jazz improvisation, but with more semicolons and fewer saxophones.
While this approach can accelerate workflows, it also introduces new risks in a discipline where correctness and reliability are of the utmost importance. Here’s a closer look at the pros and cons of vibe coding with AI for data engineering.
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Pros of Vibe Coding with AI for Data Engineering
Faster Pipeline Prototyping
Setting up new ETL jobs, Spark transformations or SQL stored procedures can be time consuming. With AI, engineers can describe what they want in plain English (“load JSON from S3″ or “flatten nested arrays”) and get a working scaffold instantly. This shortens the time from idea to prototype.
Reduced Boilerplate Burden
Much of data engineering is repetitive: writing ingestion scripts, configuring connectors, handling schema evolution. Vibe coding offloads this “grunt work” to AI, freeing engineers to focus on higher-value architectural design.
Multi-Tool Integration
Data engineers often hop between SQL, Python, PowerShell and YAML configs. AI can smooth the friction by generating idiomatic code in each language as needed, acting as a cross-technology translator.
Accelerated Debugging
Instead of manually googling error messages or digging through logs, vibe coding enables engineers to ask AI directly: “Why is my job failing with this stack trace?” Often the AI will suggest fixes immediately, keeping momentum flowing.
Learning on the Fly
For newer engineers, working alongside AI can be like having a senior engineer on call. It can explain complex joins, functions or partitioning strategies in context, building intuition while delivering working code.
Cons of Vibe Coding with AI for Data Engineering
Data Quality Blind Spots
Unlike application code, data pipelines often deal with messy real-world inputs. AI-generated transformations may “work” syntactically but overlook edge cases like missing values, schema drift or timezone inconsistencies.
Performance Pitfalls
A query that runs isn’t necessarily one that scales. AI may generate inefficient SQL joins or Spark code that works fine on sample data but grinds to a halt on terabytes of production data.
Lack of Operational Context
AI doesn’t inherently understand an organisation’s infrastructure, governance rules or SLA requirements. A vibe-coded pipeline might ignore partitioning strategies, lineage tracking or monitoring hooks, which are items critical to enterprise-grade reliability.
Overreliance Risk
Engineers may accept AI’s outputs without deeply understanding them. In a field where debugging broken pipelines at 2 a.m. is a reality, lack of comprehension can be costly.
Documentation and Governance Gaps
Vibe coding with AI is improvisational by nature. Without discipline, this can lead to poorly documented pipelines, unclear data transformations, and weak compliance with regulatory requirements like POPIA.
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When Vibe Coding with AI Works Well in Data Engineering
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Ad hoc analysis and exploration. Allows for quick testing transformations on a dataset.
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Prototyping new pipelines. Rough drafts to validate feasibility before “productionising”.
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Learning new tools. Experimenting with unfamiliar frameworks or languages.
When to Be Cautious
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Production pipelines. Where accuracy, scale, and monitoring are critical.
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Data governance workflows. Compliance requires transparency and consistency.
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Cross-team collaboration. Undocumented “vibe code” creates technical debt.
Let’s Land This
Vibe coding with AI has huge potential for data engineers. It can accelerate prototyping, reduce repetitive work, and lower the barrier to entry for complex frameworks. But data engineering isn’t just about “making code run”, it’s about scalability, reliability, and ensuring nobody’s boss is asking why yesterday’s sales data is suddenly in Klingon.
Used wisely, vibe coding is like having a hyperactive intern who never sleeps: brilliant at generating drafts, but you still need to check their work before letting them near production. Used carelessly, it’s more like giving that intern root access to your production cluster and hoping for the best (spoiler: don’t do this).
The sweet spot is treating AI as your creative sidekick, not your replacement. Let it riff ideas, sketch pipelines and spit out SQL you can actually copy-paste without crying, but always review, test, and document. Build fast, but harden carefully.
Because at the end of the day, vibe coding can help you jam through data pipelines like a rockstar… just remember that the encore is debugging at 2 a.m. if you don’t put in the guardrails.
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