AI Full Stack Data Engineer

About the role

We're looking for an engineer who builds across the full stack and owns the data underneath it. You can sit in a client session, shape the architecture, design the data foundation, and ship the application that runs on top of it — without handing off at the boundaries.

What You'll Do

• Own work end to end — from discovery and solution shaping through system design, build, and production deployment.
• Design and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production.
• Build full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable.
• Use AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality.
• Design and build AI capabilities where they fit — RAG pipelines, agentic workflows, and LLM-in-the-loop processing — and compose them via MCP servers, Skills, and Plugins.
• Orchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job.
• Stand up and own CI/CD and cloud deployments on AWS and Azure.
• Translate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences.
• Contribute reusable accelerators and technical assets back to the Data Practice.

Must Have

• Data Engineering Foundation:
• Data modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend.
• Hands-on data pipeline experience — ETL/ELT design across batch and incremental loads, built and maintained in production (not just SQL scripts on a schedule).
• Slowly Changing Dimensions (SCD) and change-data handling — knows the patterns and when each applies.
• dbt Experience— modular SQL transformations, tests, documentation, and incremental strategies.
• Advanced SQL and at least one modern data platform in depth (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse).
• Data quality thinking — testing, validation, and lineage treated as first-class, not afterthoughts.
• System design — can architect from a blank page: services, boundaries, trade-offs, and scale.
• Full-Stack AI Product Development:
• Python as a primary language — services, automation, and data work alike.
• FastAPI — async REST API design, dependency injection, testing.
• A modern frontend, ideally Next.js — component architecture, SSR, state management, and real UX sensibility.
• PostgreSQL — schema design, query optimization, indexing.
• Ways of Working:
• Comfortable in client-facing delivery — can represent TechTorch technically and translate between business and engineering.
• Customer-first mindset — anchors decisions in what the stakeholder is actually trying to accomplish, and can move fluidly between the engineer's view and the business owner's in the same conversation.
• End-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoff.

Nice to Have

• Commercial data fluency: Experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite) from opportunity to order to invoice, with the ability to diagnose, document, and resolve inconsistencies.
• Agentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management.
• RAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluation.
• Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.
• Workflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows).
• Streaming data patterns — Kafka, Spark Streaming, or Flink.
• Vector databases — Pinecone, Weaviate, Qdrant, or pgvector.
• Experiment tracking — MLflow, Weights & Biases, or similar.
• Contributions to open-source AI or data tooling, or to internal accelerators and frameworks.
• Multi-cloud or hybrid cloud architecture exposure.

What We Offer

• Fully remote — work from anywhere, globally.
• Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face.
• High-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.
• A team that takes AI tooling seriously and expects you to use it, not just name-drop it.
• Access to the full modern data and AI stack — no one-tool shops.
• Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.