Senior Data Engineer, Adtima

Data Engineers in the Adtima team build and operate the data backbone of our ads ecosystem, turning raw logs into training-ready features and production-grade model scores, and working closely with Data Scientists to make experimentation fast and reliable. They also support product teams with data for analytics tasks.

What you will do

• Build and operate the online serving data pipeline end-to-end, from raw log ingestion and feature extraction to model scoring and low-latency serving for Deep Learning models that drive ads distribution;
• Collaborate with Data Scientists to assess new requirements, surface data and resource constraints, propose alternatives, and proactively source new data to improve model quality;
• Connect and process data across teams, including dev and product, to enable shared use and analytics;
• Optimize large-scale storage and feature serving to sustain high-throughput query and aggregation cycles;
• Set up monitoring, alerting, and dashboards so pipeline issues are caught early and stakeholders can track data quality, model performance, and system health;
• Establish solid design and engineering best practices for both technical and non-technical stakeholder.

What you need

• 3+ years of working experience in Big Data and production data pipelines;
• Strong command of Python for high-performance data processing; Scala is a plus;
• Proficiency with orchestration and deployment workflows: Airflow, Docker/containerization, CI/CD, and Kubernetes (K8s);
• Proven ability to optimize storage and query performance on columnar stores (e.g., ClickHouse) through partitioning, indexing, and compression;
• Experience building dashboards and observability with tools such as Streamlit or Metabase;
• Solid skills in data ingestion, transformation, and analysis, and in synchronizing data across diverse databases for batch and stream processing;
• A proactive, research-driven mindset toward the global technology landscape and a strong willingness to adopt emerging technologies, AI-driven tools, and AI-native development in the workflow;
• Intellectual curiosity, effective communication, and strong stakeholder collaboration skills.

Nice to have

• Working knowledge of data science / ML workflows and the model lifecycle (training, evaluation, serving), and how data engineering decisions affect model accuracy;
• Domain knowledge of digital ads or experience in the advertising field;
• Experience deploying and serving LLMs in production (e.g., inference optimization, scalable serving infrastructure, latency/throughput tuning).