Backend & Data Engineer

We are looking for a strong Backend & Data Engineer to join our team and own the data and

systems layer behind both our internal AI platforms and our portfolio companies. This is a

hands-on engineering role for someone who genuinely enjoys designing data lakes,

modelling databases, building reliable ingestion pipelines and shipping production backend

services.

AI and LLM work is part of the job, but it sits on top of solid infrastructure. We need someone who can build that foundation: data gathering applications, ETL, storage architecture, APIs and the integration plumbing that turns messy real-world data into something models and products can actually use.

You will work directly with founders, internal product owners and the investment team —

designing systems for our own AI platform, supporting portfolio companies with serious data

engineering needs, and helping evaluate the technical strength of AI-driven startups we

consider investing in.

Data Infrastructure & Architecture

• Design and build the RAW Ventures data lake end-to-end — storage, partitioning, schema evolution and access patterns
• Architect relational and analytical databases (Postgres, ClickHouse, BigQuery, DuckDB or similar)
• Own data modelling, governance, cost and reliability across all sources

Data Pipelines & ETL

• Build large-scale data acquisition services — APIs, scrapers, event streams and file ingestion
• Develop and operate ETL/ELT pipelines (Airflow, Dagster, dbt, Spark or equivalent)
• Ensure robust deduplication, validation, monitoring and data-quality tooling

Backend & Systems Engineering

• Design and ship production backend services in Python and/or TypeScript/Go — REST APIs, workers, event-driven components
• Containerise and deploy via Docker/Kubernetes with CI/CD and infrastructure-as-code
• Own reliability, security and operational quality, not just features

AI / ML & LLM Integration

• Build infrastructure for LLM-based systems — RAG pipelines, vector stores, embedding and retrieval layers
• Integrate model APIs (Anthropic, OpenAI, open-source) into backend services and agent workflows
• Develop or fine-tune ML models for forecasting, NLP or recommendation and ship as stable product features

Investment & Technical Evaluation

• Support the investment team with technical due diligence on AI and data-heavy startups
• Assess architectures, pipelines, scalability and defensibility of underlying tech
• Provide technical insight to inform investment decisions and portfolio strategy

Essential Qualifications

• Strong, hands-on backend engineering experience with production systems at meaningful scale.
• Deep experience designing and operating databases — both OLTP (Postgres /MySQL) and at least one OLAP / analytical engine.
• Solid experience building data lakes, warehouses or lakehouses, and the ETL / ELT pipelines that feed them.
• Advanced Python, plus comfort with at least one of TypeScript / Node, Go or Java for backend services.
• Experience with workflow orchestration (Airflow, Dagster, Prefect or similar) and modern data tooling (dbt, Spark, Kafka, object storage).
• Working experience with LLMs, RAG pipelines, embeddings and vector databases  enough to build serious systems around them, not just call an API.
• Strong grasp of cloud infrastructure (AWS / GCP / Azure), containers, CI/CD and basic SRE practice.
• Strong problem-solving mindset and ability to operate in early-stage, fast-moving environments with shifting requirements.

Nice to Have

• Experience building data and AI products in startups, consulting or research environments.
• Exposure to sectors such as media tech, health tech, agri tech, fintech or other data heavy industries.
• Experience with optimisation, forecasting, geospatial data, time-series at scale, or graph data.
• ML framework experience (PyTorch, TensorFlow, scikit-learn) and / or model fine tuning experience.
• Comfort working across multiple parallel projects and stakeholders.

Why Join Raw Ventures

• Own the data and backend foundations behind a portfolio of technology companies.
• Work directly with founders, operators and investors on real, varied engineering problems.
• Ship AI systems that are actually used in products, not just prototyped.
• Be part of a venture environment where technology, strategy and entrepreneurship intersect.