When organizations begin their generative AI journey, they often discover that the real gap isn’t technical — it’s communicative. Teams can build smart prototypes, but connecting that with business needs and translating use cases into production pipelines is where many stalls happen.

That’s why the role of a Data Translator matters so much — especially when operating inside Snowflake’s Cortex ecosystem. These aren’t just technical folks; they’re the bridge between business teams and data engineers, helping build AI models that actually deliver ROI.

Let’s unpack what that role looks like in today’s AI transformation, why it matters, and how Snowflake Cortex makes it more powerful than ever.

Why Data Translators Are Catalysts, Not Just Interpreters

A Data Translator sits between two worlds. On one side, they understand business processes and goals — whether sales forecasting, risk reporting, or customer support triage. On the other, they understand data structure, data governance, and AI capabilities in systems like Snowflake Cortex.

Their value:

• Aligns AI use cases with real business pain points

• Reduces friction in pilots by facilitating rapid scoping and prototyping

• Ensures AI outputs are understandable, auditable, and actionable

At Snowflake, we’ve seen time and again that successful AI transformation doesn’t start with models — it starts with translation. When translators work with business teams early, pilots move faster, get supported more broadly, and turn into something real.

What Does a Data Translator Do with Snowflake Cortex?

Here’s how translators add value across the CoE AI journey:

1. Use Case Scoping and Framing

• Helps business users define success metrics (e.g., time saved, accuracy improved)

• Maps those metrics to available Snowflake data — structured or unstructured

• Shapes pilot designs using Cortex features like AISQL, RAG agents, or extractors

Outcome: A project that solves real problems — and stays achievable.

2. Prompt Engineering & Context Design

• Works with data and prompts to embed relevant context elegantly

• Ensures summarizations include traceable references and metadata

• Designs feedback loops and tuning checkpoints early in deployment

Outcome: AI outputs that users trust — not just consume.

3. Bridging Domain Logic and SQL

• Understands business terminology (e.g. ​“lost sales” vs ​“churn rate”)

• Maps it cleanly to SQL, table joins, or Cortex AI SQL functions

• Builds prompt templates that use those translations transparently

Outcome: Enables domain experts to interact using familiar language — and make sense of results.

4. Pilot Governance & Change Enablement

• Sets up RBAC rules to limit AI outputs to appropriate roles and datasets

• Embeds explainability — from lineage to user feedback — via Cortex Observability

• Drives awareness through training and demonstrations — showing how AI complements — not replaces — roles

Outcome: Less hesitation, more excitement across teams.

Real-World Example: RFP Summarization in Professional Services

One services firm used Snowflake Cortex to summarize vendor RFP responses — a repetitive, manual task. Here’s how translators helped:

• Translated ​“surprising risk clauses” into specific vendor list extractions

• Scoped PDF ingestion and metadata tagging in Snowflake

• Built a summarization agent using RAG, with post-hoc review functionality

• Afforded feedback sessions where reviewers corrected summaries and refined prompts

Because translators were embedded in the business and tech teams, the solution went from pilot to platform in weeks — not months. And users relied on the system because they helped define it.

Common Challenges and How Translators Solve Them

Challenge	Translator’s Solution
Business stakeholders don’t speak data	Translators frame language in familiar terms
Models hallucinate or miss context	Translators design prompt validation and feedback loops
Outcomes feel disconnected from daily workflows	Translators help embed AI into operational apps using Cortex Agents
Governance feels intimidating	Translators weave policies into pipelines and prompt guidelines

Without translators, value gets lost in translation — literally.

How to Empower Data Translators in Your Organization

If you’re building a Snowflake CoE or AI practice, here’s how to integrate translators effectively:

• Identify internal domain experts—people who understand process but are comfortable with data

• Provide training on Cortex tools—AISQL, extraction functions, Agents, and observability dashboards

• Create blended teams—pair translators with data engineers and business leads during pilot phases

• Recognize translators as core change agents—not just support staff; they are doing high-value alignment work

Why Snowflake Cortex Makes Translation Easier

When the platform handles both structured and unstructured data — and includes built-in features like:

• Natural language prompt execution via AISQL

• Cortex agents to orchestrate workflows across data and systems

• Explainable AI tools with lineage tracking and human review

… translators don’t have to fight tool mismatches. They can focus on bridging the human – machine gap.

This unified platform means translators aren’t just interpreting — they’re building and iterating faster, more securely, and more confidently.

Final Thought: Translation Is the Key to Adoption

AI transformation isn’t about models — it’s about meaningful change in everyday roles. Data Translators are the unsung heroes here. They turn corporate ambitions into working systems, frame outcomes in human language, and keep trust front and center.

With Snowflake Cortex, those translators get a platform that speaks their language — and lets them scale impact across the organization.

If you’re a Snowflake user or partner, think beyond just deploying AI. Think about how translators can help turn experiments into programs — and programs into transformation.