Customer Implementation Engineer

Narrative's data normalization and collaboration platform sits at the center of how the world's leading retailers, brands, and tech platforms buy, sell, and activate data. As we grow the Narrative Data Marketplace, we need a skilled Data Engineer who can turn complex datasets into reliable, scalable SQL workflows — and help customers extract maximum value from their data investments.

This is a primarily internal role with meaningful customer exposure. You'll build and optimize the SQL infrastructure that powers Marketplace operations, run data forecasts, and join select implementation calls to support strategic accounts. You're someone who thinks in queries, speaks fluent Snowflake, and is comfortable applying machine learning and AI-assisted classification to ambiguous data problems.

• Design, build, and maintain SQL workflows that support Narrative Marketplace operations — including data acquisition, normalization validation, and delivery pipelines
• Run data forecasts to model marketplace supply, demand, match rates, and revenue attribution using Snowflake and NQL (Narrative's SQL-like query language)
• Apply machine learning techniques and AI prompt engineering for data classification — assigning taxonomy, resolving ambiguous attributes, and improving row-level data quality
• Optimize Snowflake queries for performance, cost efficiency, and scalability across large datasets
• Join customer implementation calls to support onboarding, troubleshoot data pipeline questions, and translate technical outputs into business clarity
• Collaborate with the Solutions Engineering team to build repeatable frameworks for customer data health assessments
• Document SQL libraries, data dictionaries, and engineering standards to scale internal knowledge

• Strong proficiency in SQL with hands-on experience in Snowflake — including query tuning, clustering keys, materialized views, and cost optimization
• Experience building or maintaining data pipelines and transformation workflows (dbt, Airflow, or similar a plus)
• Familiarity with machine learning concepts applied to data classification — feature engineering, supervised classification, and confidence scoring
• Working knowledge of AI/LLM prompt engineering for structured data tasks such as entity extraction, taxonomy mapping, and attribute classification
• Comfort working with messy, high-cardinality datasets and ambiguous schemas — you know how to define structure where it doesn't yet exist
• Strong communication skills; able to explain technical findings clearly in customer-facing settings without losing precision
• Experience with data collaboration or marketplace platforms is a plus but not required