Senior Data Engineer (Machine Learning-focus)

WHO WE AREAdikteev is an AdTech company that helps mobile app publishers grow their revenue through targeted advertising.

• WHO WE ARE
• Adikteev is an AdTech company that helps mobile app publishers grow their revenue through targeted advertising. We deliver personalized ads that drive users to install an app or re-engage with one they've stopped using.
• Our clients are mostly mobile gaming leaders but also span e-commerce, on-demand services, and entertainment apps. Recognized for 10+ years as one of the top mobile retargeting platforms, Adikteev now operates globally from Paris, San Francisco, Barcelona and Seoul and has grown to over 120 employees.
• Beyond the business, Adikteev is consistently recognized as a great place to work. With an 18-nationality team, a 4.5-day work week, and a strong focus on inclusion, Adikteev is a place where people genuinely stay.
• WHAT YOU WILL DO
• We are looking for a Senior Data Engineer to take ownership of our ML Platform. You will help design, deploy, and evolve predictive models that power Adikteev ’s core adtech systems and you will be the architect of the infrastructure that enables our ML models to scale, perform, and deliver value at high velocity.
• You will join a high-performing team alongside Gwennaëlle, Nicolas, Pedro, and Alex. Our mission is to deliver efficient, scalable, and impactful machine learning systems — models that directly influence billions of real-time decisions per day.
• You will join a team that has full ownership of our ML ecosystem. As a Senior member, you won't just "execute" tasks. You will shape our technical DNA and take your share of the following challenges:
• Architect, scale, and maintain our data infrastructure, ensuring it meets the rigorous demands of real-time and batch ML workflows.
• Make sure data is always available: oversee processing, storage, aggregation, and API development.
• Streamline our data processes by integrating efficient testing, monitoring, alerting, and error recovery.
• Design and implement our feature store to centralize feature discovery, storage, and serving and build robust feature engineering pipelines.
• Build and optimize highly scalable pipelines using Apache Airflow and Apache Spark, focusing on high reliability and cost-efficiency.
• Contribute to innovation efforts, by exploring promising ideas from recent academic work and turning them into production-grade solutions.
• Leverage AI coding tools (Copilot, Cursor, etc.) to accelerate development cycles and stay at the forefront of engineering productivity.
• Drive best practices for data quality, CI/CD for data, and technical documentation. Mentor peers and identify infrastructure bottlenecks before they impact the business.
• WHO YOU ARE
• You might be a great fit for our Machine Learning team if you have experience in several of the following areas — don’t worry if you don’t check every box , we’re happy to consider strong candidates who are eager to learn and grow:
• Experience : Proven track record as a Data Engineer, ideally with a focus on MLOps and production-related challenges (latency, throughput, server cost optimization...).
• Technical Stack : Hands-on expertise with Apache Spark (tuning, scaling), Airflow (complex DAG orchestration). Kafka streaming and Kubernetes are a plus. Our stack runs on AWS so a good knowledge of this platform is also a plus.
• Coding Skills : You are an "AI-native" developer who embraces AI tools to write better, faster code. Hands-on experience with Python and Java/Scala is a plus.
• Data Modeling : Experience with tabular data for classification problems and deep learning is a plus.
• Feature Engineering : Solid understanding of how to transform raw data into high-quality features for machine learning. Experience building or maintaining feature stores.
• Mindset : A "Product Owner" mentality for data, you don’t just ship code; you own the reliability and scalability of the systems you build.
• Communication : Fluent in English and the ability to articulate complex architectural choices to both technical and non-technical stakeholders. Notions of French