AI-Enabled Data Engineer

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 pipeline.

Orchestration & Operations

• Orchestrate workflows across Airflow, Dagster/Prefect, Azure Data Factory, and Databricks Workflows — choosing the right tool for each job.

• Apply DataOps practices: CI/CD for data pipelines, environment promotion, infrastructure as code, and observability.

• Own the reliability of data products end-to-end — monitoring, alerting, incident response, and root cause analysis.

• Work across AWS and Azure cloud services (S3, Glue, ADLS, ADF, Synapse, Redshift) to design cost-effective, scalable architectures.

AI-Enabled Data Engineering

• Build data pipelines that feed AI systems — including RAG ingestion workflows, vector store loading, document chunking, and embedding pipelines.

• Use LLMs as active components in ETL logic: classification, entity extraction, enrichment, and data quality remediation in-flight.

• Expose data infrastructure as consumable tools for AI agents via MCP or similar agent-integration patterns.

• Use AI-paired programming (Claude Code or equivalent) as a daily productivity layer — not just autocomplete, but genuine workflow acceleration.

• Stay current on how AI tooling changes the data engineering workflow and bring those patterns back to the team.

What You Bring

Core Data Engineering: ETL/ELT Design · Data Modeling · Data Quality & Testing · Data Lineage · Batch & Incremental Loads

Data Platforms: Snowflake · Databricks · Apache Spark / PySpark · Delta Lake · Data Warehouses · Data Lakes

Transformation & Modeling: dbt Core / dbt Cloud · SQL (advanced) · Semantic Layer · Dimensional Modeling

Orchestration: Apache Airflow · Dagster / Prefect · Azure Data Factory · Databricks Workflows

AI-Enabled Engineering: RAG & Vector Store Pipelines · AI-Augmented ETL · MCP / Agent Data Tools · AI-Paired Programming · LLM Integration in Pipelines

Cloud & DevOps: AWS (S3, Glue, Redshift) · Azure (ADLS, ADF, Synapse) · CI/CD for Data · Infrastructure as Code · Python

Nice to Have

• Experience with streaming architectures: Kafka, Spark Streaming, or Flink.

• Exposure to feature stores (Feast, Tecton) or ML platform data pipelines.

• Hands-on with vector databases: Pinecone, Weaviate, Qdrant, or pgvector.

• Familiarity with data mesh or data product ownership models.

• Experience with Snowpark or Databricks AI/BI tooling.

• Building or contributing to internal data tooling, frameworks, or accelerators.

What We Offer

• Work on real, complex data problems across multiple client environments — not toy datasets.

• A team that takes AI tooling seriously and expects you to use it, not just know it.

• Access to the full modern data stack — no one-tool shops.

• Room to grow into data architecture, platform leadership, or AI engineering depending on where you want to take it.

• Collaborative culture that values opinions, craft, and intellectual curiosity.