5 Data Engineering Skills Every Company Needs for AI Success

AI is everywhere in business right now, but it only works if the data behind it is ready. A lot of AI projects stall or fail because the data is messy, incomplete, or poorly structured. That’s where data engineers come in. They make sure the data is clean, reliable, and accessible so AI can actually deliver value.

Here are five data engineering skills that every company looking to adopt AI should prioritise.

1. Data Warehousing

Data warehouses like Snowflake, BigQuery, and Redshift are where structured data is stored and organised. If you’re trying to run AI models, having well-organised historical data is essential.

A data warehouse makes it easy to pull information together across different parts of a business. It also supports analytics and reporting. Without a solid warehouse, AI teams spend more time wrangling data than building models.

2. Data Lakes and Lakehouses

Not all data is structured. A lot of what AI needs comes in raw formats, like logs, images, or sensor data. That’s why data lakes and lakehouses are becoming more common. Platforms like S3, GCS, Azure Data Lake, Delta Lake, and Databricks allow companies to store large amounts of unstructured data while keeping it organised enough for AI to use.

Lakehouses combine the best of both worlds. They let you handle unstructured data without losing the ability to query and analyse it efficiently. This can save a lot of time when preparing data for AI projects.

3. ETL and ELT Pipelines

ETL (extract, transform, load) and ELT (extract, load, transform) pipelines are the backbone of any data operation. Tools like dbt, Apache Spark, and Airflow help move data from one place to another while cleaning and transforming it along the way.

Good pipelines mean AI models get consistent, accurate data. They also make it easier to update models as new data comes in. Without them, even the best AI models can produce unreliable results.

4. Programming and Querying

SQL and Python are the two main languages data engineers use. SQL is great for querying structured data, while Python, often with Pandas or PySpark, handles more complex tasks like feature engineering for AI models.

Knowing both allows engineers to work across different types of data and make it usable for AI. It also helps them troubleshoot problems quickly, which keeps projects moving.

5. Data Governance and Observability

Finally, you need to know where your data comes from and how reliable it is. Governance and observability cover things like data quality, lineage, compliance, and monitoring. Tools such as Monte Carlo, Soda, and Bigeye are useful here.

The goal is simple: make sure your data can be trusted. Bad data will lead to bad AI outcomes. Observability tools alert you to problems early so they don’t derail your models once they are in production.

Conclusion

AI can transform a business, but it can’t work without strong data engineering. Hiring or partnering with people who understand these five skills gives you a much better chance of turning AI from an idea into something that actually delivers results.

At Raice.AI, we specialise in connecting businesses with data engineers and AI strategists who can make AI adoption practical and realistic. If you’re thinking about AI for your business, getting the right people in place from the start is the best way to succeed.