Can you imagine a world where business and digital solutions will be truly seamless and where users will help companies to co-create them? Do you want to help us to shape this human-centred world? Welcome to UNGUESS.
UNGUESS is the crowdsourcing platform for effective testing and real insights that enable tech, digital and business leaders to make smarter decisions, faster. How? Unleashing the power of the crowd, a community of highly engaged people all over the world that allows us to bring end-customer insights into the design, development, and testing phases of a product.
At UNGUESS, you’ll have the chance to make an immediate impact in a fast-paced and dynamic environment. We’re growing rapidly and strengthening our market position. Joining us now means stepping into an exciting challenge: one that won’t always be easy, but will undoubtedly be among the most rewarding and fulfilling experiences of your career. You’ll constantly learn, grow, and apply your full skill set across diverse and stimulating projects.
This is not a traditional data engineering position. Around 60–70% of your work will focus on GenAI, RAG systems, vector search, and natural language understanding (NLU). The remaining part will cover classic data engineering responsibilities such as ETL pipelines and data modeling. You won’t just maintain existing systems, you’ll be the first building block of something new, laying the foundations for a knowledge base that transforms raw testing data into intelligent, queryable insights.
As our first dedicated Data Engineer, you will be the architect of the infrastructure that makes this vision possible. You’ll own the design, implementation, and scalability of our data stack, working closely with the product and development teams.
We are a rapidly growing tech company with the ambition of building an LLM-queryable Knowledge Base by leveraging existing but currently unstructured data sources. We do not yet have a dedicated data team: this role will be the first hire, with full ownership over architecture, implementation, and scalability.
Design and implement data ingestion and normalization pipelines from heterogeneous sources (APIs, files, databases, streams).
Build a data lake on AWS (S3, Glue, Athena) and orchestrate data flows using CDK.
Implement RAG (Retrieval-Augmented Generation) systems using vector databases and LLM models (Bedrock, OpenAI, LangChain).
Model metadata and define chunking strategies for NLU-queryable documents.
Ensure data security, governance, monitoring, and cost optimization.
Collaborate with the Product team to integrate the knowledge base into the existing platform.
GenAI & Vector Search: Hands-on experience with RAG systems in production, embedding models (OpenAI, Cohere, Amazon Titan), and vector databases (OpenSearch, Pinecone, pgvector).
Strong grasp of chunking strategies, retrieval optimization (precision/recall/reranking)
Proven expertise with AWS CDK, data services (S3, Glue, Athena, Lambda, Step Functions), and ML/AI workloads (Bedrock, SageMaker). Solid understanding of IAM, KMS, VPC for security/compliance.
Has a builder's mindset and enjoys designing robust, scalable solutions.
Hands-on with serverless architectures and cost-optimized scaling strategies
Experience in cloud-native environments and CI/CD (AWS).
Familiarity with monitoring and alerting (CloudWatch, X-Ray).
Compensation: €45,000 to €50,000/year gross salary and competitive MBO bonus - this range is a guideline; we’re first and foremost looking for the right person, the final offer will be shaped around you and reflect your skills and experience.
Remote work lovers
Fast-track growth opportunities
Access to group and personal training programs
Please note that this job advertisement is open to applicants of all genders, in accordance with Laws 903/77 and 125/91.