Key Skills

Job Description

Company Description DataChef is a fast-growing consultancy founded in the Netherlands in 2019 by former AWS managers, specializing in AI, IoT, and HyperAutomation solutions that deliver measurable business value. The company is experiencing strong growth, with 60% year-over-year expansion driven by client trust and successful implementations. DataChef operates with a long-term partnership mindset, planning over a 20-year horizon and focusing on sustainable, high-quality outcomes. With over 30 “chefs” hired from the top 3% of talent, the team combines deep technical expertise with a pragmatic, delivery-focused culture. Applicants join a company that values speed without sacrificing thoroughness and encourages ongoing learning and professional development. Role Description The Senior Data Engineer will design, build, and maintain scalable data pipelines and architectures that support analytics, AI, and automation initiatives for DataChef’s clients. This full-time hybrid role is based in Amsterdam, with flexibility for partial work from home. Day-to-day responsibilities include modeling data, integrating diverse data sources, optimizing data workflows, and ensuring data quality, reliability, and security. The Senior Data Engineer will collaborate closely with data scientists, cloud architects, and client stakeholders to translate business requirements into robust technical solutions, and will contribute to best practices, documentation, and technical mentoring within the team. The role also involves evaluating new tools and technologies, supporting solution deployments, and continuously improving data infrastructure performance and cost efficiency. Qualifications Strong data engineering skills, including data modeling, ETL/ELT pipeline development, and working with batch and streaming data. Proficiency with modern data platforms and tools such as cloud data warehouses, data lakes, and orchestration frameworks (e.g., Airflow, dbt, Spark). Advanced programming skills in languages commonly used for data engineering (e.g., Python, SQL, Scala) and solid software engineering practices (testing, version control, CI/CD). Experience with cloud environments (preferably AWS, but also Azure or GCP) and related services for storage, compute, and data processing. Knowledge of analytics and AI/ML workflows, enabling effective collaboration with data scientists and ML engineers. Background in designing secure, reliable, and compliant data solutions, including monitoring, logging, and performance optimization. Excellent problem-solving, communication, and stakeholder management skills, with the ability to explain complex concepts to non-technical audiences. Experience in consulting or client-facing roles, including requirements gathering and translating business needs into technical designs. Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience. <

Core Responsibilities

Design, build, and maintain scalable data pipelines and architectures for client analytics, AI, and automation initiatives, ensuring data quality, reliability, security, and efficient performance. Collaborate with data scientists, cloud architects, and client stakeholders to develop technical solutions, support deployments, evaluate tools, document best practices, and mentor team members.

Requirements

Requires strong data engineering experience with data modeling, ETL/ELT, batch and streaming systems, modern data platforms, and programming in Python, SQL, or Scala. Candidates should have cloud experience, sound software engineering practices, knowledge of analytics and AI/ML workflows, client-facing consulting skills, and a relevant degree or equivalent practical experience.