Backend & Data Engineer

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    "description": "<p>We are looking for a strong Backend &amp; Data Engineer to join our team and own the data and</p>\n<p>systems layer behind both our internal AI platforms and our portfolio companies. This is a</p>\n<p>hands-on engineering role for someone who genuinely enjoys designing data lakes,</p>\n<p>modelling databases, building reliable ingestion pipelines and shipping production backend</p>\n<p>services.</p>\n<p>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.</p>\n<p><br></p>\n<p>You will work directly with founders, internal product owners and the investment team —</p>\n<p>designing systems for our own AI platform, supporting portfolio companies with serious data</p>\n<p>engineering needs, and helping evaluate the technical strength of AI-driven startups we</p>\n<p>consider investing in.</p>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Data Infrastructure &amp; Architecture</span></p>\n<ul>\n<li>Design and build the RAW Ventures data lake end-to-end — storage, partitioning, schema evolution and access patterns</li>\n<li>Architect relational and analytical databases (Postgres, ClickHouse, BigQuery, DuckDB or similar)</li>\n<li>Own data modelling, governance, cost and reliability across all sources</li>\n</ul>\n<p><span style=\"font-weight: bold\">Data Pipelines &amp; ETL</span></p>\n<ul>\n<li>Build large-scale data acquisition services — APIs, scrapers, event streams and file ingestion</li>\n<li>Develop and operate ETL/ELT pipelines (Airflow, Dagster, dbt, Spark or equivalent)</li>\n<li>Ensure robust deduplication, validation, monitoring and data-quality tooling</li>\n</ul>\n<p><span style=\"font-weight: bold\">Backend &amp; Systems Engineering</span></p>\n<ul>\n<li>Design and ship production backend services in Python and/or TypeScript/Go — REST APIs, workers, event-driven components</li>\n<li>Containerise and deploy via Docker/Kubernetes with CI/CD and infrastructure-as-code</li>\n<li>Own reliability, security and operational quality, not just features</li>\n</ul>\n<p><span style=\"font-weight: bold\">AI / ML &amp; LLM Integration</span></p>\n<ul>\n<li>Build infrastructure for LLM-based systems — RAG pipelines, vector stores, embedding and retrieval layers</li>\n<li>Integrate model APIs (Anthropic, OpenAI, open-source) into backend services and agent workflows</li>\n<li>Develop or fine-tune ML models for forecasting, NLP or recommendation and ship as stable product features</li>\n</ul>\n<p><span style=\"font-weight: bold\">Investment &amp; Technical Evaluation</span></p>\n<ul>\n<li>Support the investment team with technical due diligence on AI and data-heavy startups</li>\n<li>Assess architectures, pipelines, scalability and defensibility of underlying tech</li>\n<li>Provide technical insight to inform investment decisions and portfolio strategy</li>\n</ul>\n<p><br></p>\n<p><span style=\"font-weight: bold\">Essential Qualifications</span></p>\n<ul>\n<li>Strong, hands-on backend engineering experience with production systems at meaningful scale.</li>\n<li>Deep experience designing and operating databases — both OLTP (Postgres /MySQL) and at least one OLAP / analytical engine.</li>\n<li>Solid experience building data lakes, warehouses or lakehouses, and the ETL / ELT pipelines that feed them.</li>\n<li>Advanced Python, plus comfort with at least one of TypeScript / Node, Go or Java for backend services.</li>\n<li>Experience with workflow orchestration (Airflow, Dagster, Prefect or similar) and modern data tooling (dbt, Spark, Kafka, object storage).</li>\n<li>Working experience with LLMs, RAG pipelines, embeddings and vector databases  enough to build serious systems around them, not just call an API.</li>\n<li>Strong grasp of cloud infrastructure (AWS / GCP / Azure), containers, CI/CD and basic SRE practice.</li>\n<li>Proficiency in spoken and written Russian.</li>\n<li>Strong problem-solving mindset and ability to operate in early-stage, fast-moving environments with shifting requirements.</li>\n</ul>\n<p><span><br></span><br></p>\n<p><span><span style=\"font-weight: bold\">Nice to Have</span></span></p>\n<ul>\n<li>Experience building data and AI products in startups, consulting or research environments.</li>\n<li>Exposure to sectors such as media tech, health tech, agri tech, fintech or other data heavy industries.</li>\n<li>Experience with optimisation, forecasting, geospatial data, time-series at scale, or graph data.</li>\n<li>ML framework experience (PyTorch, TensorFlow, scikit-learn) and / or model fine tuning experience.</li>\n<li>Comfort working across multiple parallel projects and stakeholders.</li>\n</ul>\n<p><br><br></p>\n<p><span style=\"font-weight: bold\">Why Join Raw Ventures</span></p>\n<ul>\n<li>Own the data and backend foundations behind a portfolio of technology companies.</li>\n<li>Work directly with founders, operators and investors on real, varied engineering problems.</li>\n<li>Ship AI systems that are actually used in products, not just prototyped.</li>\n<li>Be part of a venture environment where technology, strategy and entrepreneurship intersect.</li>\n</ul>",
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