Why ETL (or ELT) is Now a Product Manager’s Core Responsibility?

The Data Pipeline is Your Product’s Foundation

As Product Managers, our focus is often on user features and growth metrics. But beneath every critical decision, every personalized experience, and every generative AI feature, there is a data pipeline. This pipeline, traditionally known as ETL (Extract, Transform, Load), is the unseen engine that creates the clean, quality data required for success.

If you want to train the next machine learning model, build tables for executive reports, or bring rigor to compliance requirements, you need a reliable pipeline. ETL is no longer just a data engineering function; it is foundational to modern product strategy.

From ETL to ELT: The Strategic Shift

The classic ETL process—where data is transformed before it’s loaded into a warehouse—is rapidly being supplanted by ELT (Extract, Load, Transform).

The shift is driven by economics: storage is cheap. We now live in an era of "store first, act later," where the priority is to dump all raw data into a staging resource before transformation. This approach gives us maximum flexibility for future analysis and is supported by modern architectures like the Medallion Architecture (Bronze, Silver, Gold layers) and time travel capabilities for data recovery.

For the Product Manager, this means:

1. Flexibility: We retain all data, even if its value isn't immediately known.

   
   

2. Traceability: We can audit and correct transformation logic without losing the raw source data.

Today's ETL/ELT pipelines must adapt to two major trends that directly impact product value:

1. The AI/LLM Connection: The hype around Generative AI (GenAI) and Large Language Models (LLMs) is undeniable, but their success hinges on data pipelines. The true value in LLMs comes from training or fine-tuning them on clean, curated datasets. In this sense, AI is literally built on data transformation. As Product Managers, we must ensure our pipelines are robust enough to create high-quality, consistent datasets at scale, directly enabling our AI features.

   
   

2. The Rise of the Data Lakehouse: The industry is converging on the Data Lakehouse architecture, which seamlessly merges the benefits of data warehouses (for analytics) and data lakes (for AI workloads).

Why it Matters:

1. The Lakehouse model reduces the complexities associated with maintaining parallel architectures, ensuring consistent data governance, and minimizing duplication.

   
   

2. Handling Velocity: This architecture is essential for handling streaming data—the vast quantities of real-time data generated by modern applications and sensors—which forces us to build and manage continuous pipelines rather than relying solely on traditional batch processing.

The PM’s Mandate: Driving Maintainability and Observability

A pipeline is only as good as its health. The ultimate objective of any ETL/ELT pipeline is to deliver business value. My focus as a Product Manager is ensuring the system can achieve this by demanding high standards for pipeline health by:

1. Maintainability & Robustness: A well-designed pipeline must be resilient, with fast recovery times and minimal resource allocation toward troubleshooting.

   
   

2. Observability: We need effective issue identification, monitoring, and benchmarking to quickly discern whether an anomaly was introduced by an analyst or a technical issue. This demands strong data lineage that bridges the gap between raw ingestion and cleaned data.

   
   

3. Backfilling Logic: Any work done once should be repeatable. I ensure pipelines are designed with backfill logic to easily simulate historical runs or recreate lost data without manual effort.

The complexity of data engineering extends far beyond just building the fastest pipelines; it encompasses resource management, trade-offs, and strong collaboration. By mastering the strategic implications of modern ETL/ELT, a Product Manager can directly accelerate the delivery of high-quality, scalable product features.

Conclusion:

A modern product is only as powerful as the data that fuels it. The shift to ELT, the rise of the Lakehouse, and the demands of AI all hinge on a single, critical asset: a robust, observable data pipeline.

As Product Managers, we must stop viewing pipelines as a technical backend and start treating them as a core strategic advantage. Investing in this foundation isn't just about data quality—it's about building products that are truly ready for the future.