5 Data Engineer Jobs Berlin

ADMET Data Pipeline Development: Design, build and maintain scalable pipelines for ingesting, processing, and harmonizing diverse ADMET datasets from public sources (e.g., ChEMBL, PubChem) and proprietary assays., Data Harmonization: Standardize heterogeneous ADMET data formats (e.g., in vitro assays, in silico predictions) across network participants to enable modelling readiness of the data, Model-Ready Dataset Curation: Preprocess raw ADMET data (e.g., normalizing units, handling missing values) to support AI/ML model training for a variety of endpoints (like bioavailability, hERG inhibition, or CYP450 interactions), Data Quality Assurance: Implement and automate validation checks to ensure ADMET data integrity, Cross-Functional Integration: Work with computational chemists to optimize data structures for AI-driven ADMET models (e.g., graph-based representations for metabolic pathways), Work with our customers and potentially academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving ADMET data, including leveraging and harmonizing assay data from different sources., Collaborate cross-functionally to ensure data and resulting models address real-world drug discovery needs., Mentor and guide team members on a content level, supporting the planning and breakdown of complex ADMET data preparation., Influence strategic decisions on data infrastructure and data quality assurance, Contribute to publications or open-source contributions where relevant.

Optimize end-to-end data pipeline performance, Develop and maintain scalable data processing solutions and backends, Integrate data sources using modern ETL/ELT practices, Clean and transform raw data using dbt, Build and maintain robust data quality frameworks and validation processes, Maintain and troubleshoot data architecture and infrastructure, Design, enhance, and maintain data processes and security best practices, Support ML & AI Engineers in building efficient data pipelines, Contribute to AI-driven applications

Build and maintain data transformations for downstream consumption (ETL, ELT, streaming), Negotiate interfaces between software producers and data consumers, Work with social impact data to portray how education systems evolve over time, Use streaming pipelines and batch processing technologies to handle large amounts of data, Research and learn new technologies to adopt best practices, Collaborate cross-functionally with stakeholders, data scientists, BI analysts, engineers, product owners, and users, Drive the design and implementation of new features, Own data engineering projects from start to end, Manage different portions of critical data infrastructure

Assisting teams with data-related tasks, including data management, processing, and analysis, Contributing to the administration and maintenance of our PostgreSQL database to ensure optimal performance, security, and reliability, Collaborating with teams across Agora, where opportunities may arise to support and enhance communication strategies by leveraging data processing and visualisation for additional analytical insights, Support engagement with Agora's partner network and the wider energy data and modelling community, sharing best practices and supporting knowledge and capability development

Weiterentwicklung und Skalierung unseres Self-Service Data Hubs auf Basis von Microsoft Fabric, Implementierung von Datenpipelines und ETL-Prozessen mit PySpark und SQL, Integration und Nutzung von KI-Technologien (z.B. Azure AI), um datenbasierte Anwendungen zu erweitern und zu verbessern, Zusammenarbeit mit Fachabteilungen und Datenverantwortlichen, um den Self-Service Data Hub optimal auf deren Bedürfnisse auszurichten, Unterstützung bei der Einführung und Weiterentwicklung des Data Mesh Ansatzes innerhalb der Apleona Gruppe

Entwicklung von KI-Assistenten für Normen-Anwender und innerbetriebliche Prozesse der DIN-Gruppe mithilfe von Large Language Models (LLMs) und Retrieval-Augmented Generation (RAG), Anwendung von Natural Language Processing (NLP) Methoden, Anpassung von Machine Learning Modellen und LLMs, um Wissen aus Normen-Daten automatisch zu extrahieren und zu strukturieren, Planung, Entwicklung, Betrieb und Optimierung von Infrastrukturen mit Hilfe von DevOps, LLMOps, und Cloud-Diensten, um die Bereitstellung und den Betrieb zukünftiger KI-Lösungen sicherzustellen, Arbeiten in interdisziplinären Teams, um Entwicklungen in bestehende und neue Produkte und Lösungen der DIN Media zu integrieren, Entwicklung von Prototypen und Bewertung von Technologien, die als Grundlage für neue und innovative Produkte und Lösungen dienen, Projektmanagement und Projektsupport für KI-Projekte mit externen Dienstleistern, Mitarbeit in DIN- und KI-Netzwerken und die Unterstützung bei DIN-gruppenweitem Wissensaufbau im Bereich Künstliche Intelligenz

Data Analysis: Clean and analyze data to identify patterns and trends, uncovering insights that drive our solutions., Statistical Analysis: Use statistical techniques to interpret data, ensuring the reliability and accuracy of your findings., Data Science: Create data models to predict heating system behavior and patterns using advanced analytics techniques and machine learning., Algorithm Development: Create production-ready algorithms and data pipelines to optimize energy system components, reducing CO2 emissions and enhancing energy efficiency., Collaboration: You will be responsible for the entire process of developing product features, from understanding customer requirements to final feature deployment. This requires seamless collaboration with energy engineers, frontend and backend developers, product and customer success managers., Research Support: Conduct experimental data analysis to refine and improve energy optimization algorithms., Clear Communication: Present findings in a concise and understandable manner., Additional tasks: Support our team in creating data models to predict energy time series data (e.g. heat demand, solar production, electricity consumption) and in optimizing energy systems using machine learning algorithms ((Un)supervised and Reinforcement Learning).