TechTorch

Data

AI-EnabledDataEngineer

$115–165k ~AI est. United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“AI-Enabled Data Engineer at TechTorch”

Industry & Context.

Data
Full Job Description

About TechTorch TechTorch is a high-growth enterprise technology consultancy that partners with the world’s leading private equity-backed businesses. We deliver AI-powered solutions, accelerators, and data-driven transformation initiatives that drive measurable value at speed and scale. Our mission is to redefine enterprise technology consulting for private equity. We combine the agility of a scale-up with the discipline and rigor demanded by the most sophisticated investors and operators. TechTorch was founded by seasoned leaders — including former Bain consultants, CIOs, and tech executives — with deep expertise in technology, transformation, and value creation. We were built to deliver results that matter. About the Practice     TechTorch’s Data Practice builds the data infrastructure, platforms, and pipelines that enable organizations to move from raw data to measurable business value. We work across the full data stack — from ingestion and modeling to AI-ready data products — and we move fast by letting AI do the heavy lifting wherever it can. This role sits at the intersection of deep data engineering craft and modern AI capability. Data engineering is your foundation. AI is your force multiplier.   What You’ll Do     Data Engineering & Platform - Design, build, and maintain scalable data pipelines and ETL/ELT workflows across cloud and on-prem environments. - Work with Snowflake, Databricks, and Delta Lake as primary data platforms — handling ingestion, transformation, storage optimization, and access patterns. - Model data with dbt: write modular SQL transformations, manage dependencies, enforce data contracts, and maintain documentation. - Build and maintain semantic layers that serve consistent, governed metrics to downstream consumers. - Design data warehouse schemas and data lake structures that balance performance, cost, and queryability. - Implement data quality frameworks — testing, validation, alerting, and lineage — as first-class citizens in every

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