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.
Skills Required
- Design and maintain ETL/ELT data pipelines and data modeling
- Experience with Snowflake
- Experience with Databricks and Delta Lake
- Advanced SQL and dbt Core/dbt Cloud for transformations and modeling
- Apache Spark / PySpark
- Orchestration with Airflow, Dagster, Prefect, or Azure Data Factory
- Cloud experience: AWS (S3, Glue, Redshift) and Azure (ADLS, ADF, Synapse)
- Implement data quality frameworks, testing, validation, and lineage
- CI/CD for data pipelines and Infrastructure as Code
- Python
- Build AI-enabled pipelines: RAG ingestion, document chunking, embeddings, vector store loading
- Integrate LLMs into ETL for classification, entity extraction, enrichment
- Experience building semantic layers and dimensional/data warehouse schema design
- Experience with vector databases (Pinecone, Weaviate, Qdrant, pgvector)
- Streaming architectures: Kafka, Spark Streaming, or Flink
- Feature stores or ML platform data pipelines (Feast, Tecton)
- Familiarity with Snowpark or Databricks AI/BI tooling
- Experience contributing to internal data tooling, frameworks, or accelerators
TechTorch Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about TechTorch and has not been reviewed or approved by TechTorch.
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Affordable Benefits — Health coverage is described as covering nearly all employee premiums, reducing out-of-pocket costs. Dental and vision are included with the same high employer contribution.
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Leave & Time Off Breadth — Time off is framed as unlimited, with a recommended minimum of three weeks per year to encourage actual usage.
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Parental & Family Support — Parental leave provides fully paid time off of 12 weeks for primary caregivers and 8 weeks for secondary caregivers.
TechTorch Insights
What We Do
At TechTorch, we lead the way in AI-powered Enterprise Technology (ET) solutions and services for Private Equity-backed businesses. Our mission is to operationalize winning strategies in commercial excellence, enterprise data, and AI use cases to drive successful outcomes at unparalleled speed. Every year, over $1 trillion is spent by companies on defining, designing, and implementing ET solutions. Yet, 80% of these efforts fail to achieve their intended business objectives. Despite advances in the software environment, the digitization of businesses with modern Enterprise Technology remains a manual and painful experience, one that we believe is ripe for disruption. TechTorch is at the forefront of this disruption, enhancing the success, reducing the cost, and accelerating the speed of digitization journeys. We deliver ET services and solutions powered by AI accelerators, such as pre-configured digital use cases and architectures. TechTorch provides solutions in a fraction of the time and cost compared to traditional IT service providers.
