Key Skills

Job Description

About the Company This opportunity is with an early-stage AI company developing technology for the finance sector. The company is building an AI-powered platform designed to help businesses make better use of financial and operational data spread across accounting systems, ERP platforms, billing tools, spreadsheets, and other sources. Its focus is on building reliable data foundations that allow AI agents to work with connected business information and support meaningful decisions. The technology combines data engineering, knowledge graphs, vector search, and GraphRAG to make information more accessible and useful to AI-driven financial applications. About the Role The company is looking for a Senior Data Platform Engineer to build and maintain the data infrastructure behind its AI products. Working directly with the incoming CTO, you will help shape the platform's architecture, develop reliable data pipelines, integrate external systems, and ensure that data is accurate, consistent, and ready for use by AI agents. This is a hands-on engineering role that combines backend development, data platform engineering, cloud infrastructure, and AI-related data technologies. You will work with Python, SQL, PostgreSQL, and modern data engineering tools, while contributing to the development of knowledge graphs, embeddings, retrieval systems, and GraphRAG capabilities. As the engineering team grows, there may also be opportunities to take on team leadership and help build out the data engineering function. Key Responsibilities Build and maintain data ingestion pipelines that bring information together from financial and operational systems. Develop external API integrations to connect accounting platforms, ERP systems, billing tools, and other data sources. Use Python, SQL, and PostgreSQL to process, transform, and manage structured business data. Design effective data models that support reliable data access and downstream AI applications. Develop and maintain ETL/ELT workflows to ensure data is cleaned, connected, and available for product use. Build data infrastructure that supports AI agents and enables dependable access to financial and operational information. Contribute to knowledge graph development, entity relationships, embeddings, retrieval systems, and GraphRAG. Work with graph and vector database technologies, including SurrealDB, to support AI-driven data retrieval. Apply production engineering practices, including automated testing, CI/CD, monitoring, and infrastructure management. Use Terraform and cloud technologies to support reliable and maintainable platform infrastructure. Make architectural decisions and contribute directly to the implementation of the data platform. Work closely with the incoming CTO and the existing engineering team on technical direction and platform development. Help develop the data engineering function and potentially take on technical leadership as the company grows. Required Qualifications Approximately 6–10 or more years of experience building backend systems, data platforms, or machine learning infrastructure. Strong programming skills and experience with Python and SQL. Experience with PostgreSQL, data modelling, ETL/ELT processes, and data ingestion pipelines. Experience integrating external APIs and working with data from multiple systems. Cloud engineering experience, ideally with Google Cloud Platform (GCP). Understanding of production engineering practices, including testing, CI/CD, monitoring, and infrastructure as code. Ability to make architectural decisions and build reliable systems that can support production applications. A hands-on approach to engineering, with the ability to take ownership of implementation rather than focusing solely on architecture and planning. Understanding of data quality and reliability requirements for AI-powered products. Preferred Qualifications Experience developing AI-powered products or working with machine learning infrastructure. Knowledge of knowledge graphs, entity relationships, GraphRAG, embeddings, and retrieval systems. Experience with Neo4j or similar graph database technologies. Familiarity with SurrealDB or databases that combine graph and vector capabilities. Interest in taking on technical leadership responsibilities and helping build a growing data engineering team. Location and Work Arrangement Location: Amsterdam, Netherlands. Work Arrangement: Remote-first, with an expectation that the team ideally meets in Amsterdam once a week. Compensation & Benefits Base Salary: €95,000–€115,000 per year, with the original listing also stating a range of €100,000–€115,000. Equity: 0.5%–0.75%. Direct collaboration with the incoming CTO and meaningful input into platform architecture and engineering decisions. Potential to progress into a team lead role and help build the data engineering function as the company grows.

Core Responsibilities

Build and maintain data pipelines, integrations, models, and cloud infrastructure that connect financial and operational systems and provide reliable data for AI products. Contribute to knowledge graphs, embeddings, retrieval systems, and GraphRAG while shaping platform architecture and potentially helping lead the growing data engineering function.

Requirements

Requires approximately 6–10 or more years of experience building backend systems, data platforms, or machine learning infrastructure, with strong Python, SQL, PostgreSQL, data modeling, pipeline, and API integration skills. Candidates should also have cloud and production engineering experience, architectural judgment, and a hands-on approach; AI, graph database, and GraphRAG experience is preferred.

Benefits

• Equity
• Direct Collaboration With the CTO
• Input Into Platform Architecture
• Potential Team Lead Progression