Tendios

Data Engineer

• Propose and build data-driven product features such as market and competitor intelligence, pricing benchmarks, contracting-body profiles, tender matching, alerting, and data-quality signals
• Own the data side of AI features
• Design and improve the retrieval layer behind Vera and the Dynamic RAG service, including document ingestion, conversion, chunking, embeddings, Qdrant indexing, Elasticsearch hybrid search, and reranking
• Use LLMs for structured extraction, classification, entity resolution, and summarisation
• Build evaluation sets to measure retrieval and answer quality
• Provide clean, structured, documented data and tools for Vera and the Tendios MCP server
• Define and measure data and retrieval quality, including source coverage, freshness, parsing accuracy, deduplication, and RAG answer quality
• Own and improve the collection pipeline, including Crawl Manager, Tenders Discovery, Collector, parsers, and RabbitMQ consumers
• Improve pipeline reliability, observability, and cost efficiency
• Maintain consistent read models in Elasticsearch and Qdrant
• Build the analytics layer on ClickHouse, orchestrated with Prefect and exposed through Metabase
• Replace manual Excel-based SaaS metrics reporting with a warehouse for MRR, churn, and NRR
• Write production Python for pipelines and data services
• Write TypeScript/Node.js in the NestJS Turborepo backend where data features touch the API
• Review code, set standards for data modelling and migrations, and document decisions in Confluence
• Contribute to data governance and security work related to ENS and ISMS compliance
• Report directly to the Head of Technology and collaborate with product, AI & Data, and backend squads

Senior Data Engineer

Full-stack Engineer

• Own Tendios data end to end, from scraping through customer-facing products and AI features
• Propose and build data-driven features such as market intelligence, pricing benchmarks, contracting-body profiles, tender matching and alerting, and data-quality signals
• Design and improve the retrieval layer behind Vera and Dynamic RAG, including document ingestion/conversion, chunking, embeddings, Qdrant indexing, Elasticsearch hybrid search and reranking
• Use LLMs for structured extraction, classification, entity resolution and summarisation
• Build evaluation sets and measure retrieval and answer quality
• Provide clean, structured, documented data and tools for Vera and the Tendios MCP server
• Define and measure data quality, retrieval quality, coverage, freshness, parsing accuracy and deduplication
• Own and improve the collection pipeline, including Crawl Manager, Tenders Discovery, Collector, parsers and RabbitMQ consumers
• Maintain consistent read models in Elasticsearch and Qdrant
• Build the ClickHouse analytics layer, orchestrated with Prefect and exposed through Metabase
• Replace manual Excel-based SaaS metrics reporting with a proper warehouse
• Write production code in Python and TypeScript/Node.js within the NestJS Turborepo backend
• Review code, set standards for data modelling and migrations, and document decisions in Confluence
• Contribute to data governance and security, including ownership, retention, access and auditability
• Report directly to the Head of Technology and collaborate with product, AI & Data, and backend squads

Senior Data Engineer

Desarrollador/a Fullstack