The case for spec-driven development in analytics engineering

I recently came across a Harvard Business Review article called “AI Makes Building Easy. Choosing What to Build Is Harder.” The authors describe an experiment where teams of NYU Stern students used AI tools to tackle complex real-world problems. While AI compressed the work of building, problem framing and decision-making remained central to getting somewhere useful.

The same shift is showing up in analytics engineering. Writing transformation code is getting easier, while defining the business logic that code needs to express is still hard.

Spec-driven development (SDD) gives that upstream work a concrete form. Before implementation begins, the team creates a structured spec for what the data pipeline should produce, how business rules should be handled, which decisions have been agreed, and what evidence will prove the output is correct. The spec becomes the bridge between business requirements and implementation.

Why analytics engineering is a strong fit for Spec-driven development

A common objection to defining requirements before implementation is that teams learn what users need by shipping. That makes sense for many product decisions. A team may need to put a workflow in front of users, observe how people use the workflow, and revise the design.

Analytics engineering often works differently, especially when a data pipeline encodes finance or operational logic. When someone asks for net revenue by segment, there is a right answer. There is a policy on when a contract is recognized, what happens to credits, how refunds are netted.

Code can express one interpretation perfectly and still produce the wrong business result.

Those rules usually exist before the data pipeline does. They may live in policy, a spreadsheet, a dashboard, a system configuration, or the working knowledge of the person who has owned the process for years. Different teams may also hold conflicting versions of the same rule. In both cases, the work starts with discovery. Creating a spec before developing can ensure this discovery work is done before a coding agent confidently provides an incorrect answer.

Structure turns context into requirements

A meeting transcript can contain valuable business context, but it still leaves plenty of room for interpretation by the person or agent implementing the data pipeline. A useful spec converts that discussion into explicit requirements.

For data engineering, a few elements matter especially:

• Definitions with an owner and an authoritative source: A metric definition should identify the rule, who owns the decision, and where the supporting source lives.
• Transformation rules and mappings: Joins, entity mappings, hierarchies, filters, calculations, and source precedence should be stated precisely enough to implement.
• Edge cases with expected behavior: Returns, credit memos, backdated changes, currency handling, late-arriving records, and other exceptions should each have an expected treatment.
• Open decisions: When a definition or source-of-truth conflict remains unresolved, the spec should expose the decision so the right owners can settle it before implementation.
• Validation criteria tied to known business results: A successful query proves that the code executes. A stronger test establishes whether the output matches an agreed business figure or expected behavior, such as Q4 net revenue matching the GL within an agreed tolerance.

The value of this structure goes beyond making requirements easier to read. Filling in an owner, source, edge case, or validation criterion often reveals a decision the team has never explicitly made. The spec makes that gap visible early enough to resolve deliberately.

As implementation gets easier, the spec matters more

When writing transformation code consumed most of the effort, spec work could feel like overhead. The person writing the code could make judgement calls on business knowledge based on their expertise, and identify logic that needs to be validated or confirmed by the business.

Now that coding agents do most of the lift, implementation is faster, but agents need a way to know the business logic that needs to be encoded and verified.

Spec-driven development can give agents that structure: find the business rules, resolve the open decisions, define what correct means, then build against the spec.