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 β
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 β 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 β 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 β 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 β 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 β 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.
Apply To This Job
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 β
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 β 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 β 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 β 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 β 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 β 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.
Apply To This Job