Sthenos AI is the AI developer of EFA Group, building intelligent, mission-ready solutions for defense and aerospace. With deep expertise in Command-and-Control (C2), cyber defense, computer vision, and autonomous systems, we design and deploy secure, field-proven AI that enhances operational efficiency and situational awareness. As part of a leading European defense ecosystem, we bring scalable innovation where it matters most — in the theater of operations.
As a Data Readiness Engineer (f/m/d), you assume ownership of the end-to-end preparation of data for AI projects within a dynamic cross-functional environment of data scientists, ML engineers, and domain experts. You work closely with product teams, business stakeholders, and external partners to ensure that high-quality, well-documented, and fit-for-purpose data is available so that AI projects can start efficiently and reliably:
You own the end-to-end preparation of data for AI and data science projects, from understanding the use case and data requirements through data sourcing, evaluation, curation, and provisioning.
You identify, acquire, and integrate relevant data sources, including internal, external, open, and synthetic data, in alignment with project and business needs.
You assess data quality, completeness, bias, risks, and suitability for specific AI and data science use cases and document your findings transparently.
You create dataset ready packs per use case like raw snapshots, curated versions, schemas, data dictionaries, quality reports etc. with the goal of enabling their reuse across projects.
You ensure that prepared datasets are well-documented (metadata, data dictionaries, lineage, assumptions, limitations) so that data science and AI teams can immediately start working with them.
You liaise with Sthenos AI’s management and create strong partnerships within the EFA and Theon Groups to understand the product strategy of the company and be one step ahead when it comes to data acquisition.
You collaborate closely with data scientists and ML engineers to understand modeling needs and translate them into concrete data requirements.
You actively support AI project planning by estimating data availability, readiness, and effort early in the project lifecycle.
You continuously improve data preparation processes, standards, and best practices to reduce friction and increase scalability across AI projects.
You monitor and screen the market for new data sources, data acquisition methods, and tooling for data quality management, labeling, and synthetic data generation.
You ensure compliance with legal, ethical, and security requirements (e.g., data protection, licensing, usage constraints), especially in sensitive and regulated domains.
You create reports of data-related insights, risks, and recommendations and communicate them clearly and in a recipient-appropriate way to technical and non-technical stakeholders.
You scout together with the Product Owner new possibilities for AI projects within the Group.
Your Skills
Graduate degree in Data Science, Computer Science, Information Systems, Data Management, Statistics, or a related quantitative field.
4+ years of professional experience in hands-on data management, data engineering, data analytics, or a closely related role.
Strong understanding of data sourcing, data quality assessment, and data lifecycle management.
Hands-on experience working with structured, semi-structured, and unstructured datasets at scale.
Good knowledge in Python scripting and common data libraries (e.g., Pandas, NumPy); SQL proficiency is required.
Experience with data storage solutions and data repositories (e.g., data lakes, data warehouses, object storage).
Familiarity with data labeling, annotation workflows, and synthetic data generation is a strong plus.
Experience working closely with data science and machine learning teams on AI-driven projects.
Knowledge of data governance, metadata management, and data documentation best practices.
Familiarity with cloud platforms and their data services is advantageous.
Basic understanding of modern AI techniques (e.g., computer vision, large language models) and their data requirements is a strong plus.
Your Competencies
Strong sense of ownership and responsibility for data quality and data readiness.
Analytical mindset with the ability to structure complex, ambiguous data problems.
Pragmatic and solution-oriented, with a strong focus on enabling others to work efficiently.
Comfortable working at the intersection of technical, business, and regulatory requirements.
Ability to communicate clearly with both technical and non-technical stakeholders.
Curiosity and motivation to continuously improve data processes, tools, and standards.
We Offer: