8 Data Engineer Jobs Remote

Design, develop, and maintain robust data pipelines to support reporting, analytics, and decision-making across the organization., Collaborate closely with data engineers, analysts, and scientists to translate business requirements into scalable data engineering solutions., Build and maintain scalable data infrastructure and processes, ensuring reliability, efficiency, and alignment with industry best practices., Monitor, optimize, and troubleshoot database performance to ensure seamless data flow and minimal downtime., Enable advanced analytics and machine learning by designing cloud-based solutions that are scalable and efficient., Guarantee data quality and consistency, contributing to the creation of a single source of truth across the organization., Play a pivotal role in shaping how data powers key business decisions in a fast-paced e-commerce environment.

Automate investment and community processes, Enhance data democratization and governance for the investment and community team, Develop and expand existing data pipelines within the data processing system including AI copilot and data visualization, Tackle non-trivial tasks alongside modern infrastructure development, Learn from the Head of Engineering by working side-by-side with him

Konzeptionierung, Entwicklung und Implementierung von Tools zur intelligenten Steuerung grüner Wärme, Durchführung komplexer Datenanalysen und Modellierungsaufgaben zur Unterstützung der Produktentwicklung, Analyse und Interpretation von Energiedaten auf allen Ebenen: Von Zeitreihen der Datenerfassung bis zur Auswertung für strategische Entscheidungen, Aufbereitung, Analyse und Visualisierung von Kundendaten

Build and maintain data processing pipelines and storage, Ensure high performance and cost-efficiency when training and executing machine learning models, Support in automating manual processes, Support in maintaining high data quality

Create and implement processes to extract, cleanse, and consolidate data from diverse sources ensuring it is ready for analysis., Analyze large and complex data sets to extract valuable insights that inform key business decisions and enhance overall strategy., Present business recommendations derived from your analysis and effectively communicate findings in a clear and accessible way to relevant stakeholders., Identify upcoming analytical needs and translate them into actionable tracking requirements to support and optimize the company’s planning processes and commercial strategies., Deliver data-driven dashboards while developing key performance indicators (KPIs) to monitor and improve business performance., Uncover critical insights and effectively communicate them to stakeholders., Apply advanced analytic methodologies, utilize visualization tools, support the creation of analysis, KPIs and dashboards or write customized reports.

Collaborate closely with project managers, applied scientists and machine learning engineers, as well as medical technical assistants in the labs of our clinical partners, Improve and scale the data pipelines connecting clinical partners with the big-data SaaS platform, Clean and curate clinical datasets, Use Jupyter for analytics and discovery, Maintain platform services using DevOps skills, Extract structured data from text using machine learning and heuristics, Perform code reviews, ensuring maintainability, reliability and scalability

Create and optimise machine learning models in Python to address practical business challenges, ensuring high performance and accuracy, Architect, implement, and manage robust data analysis pipelines to efficiently process and analyze large datasets, enhancing data accessibility and usability, Engage in impactful projects such as time series analysis for demand forecasting, building recommendation systems, and customer behavior analytics to drive actionable insights and business value, Propose and implement innovative ideas for new use cases in collaboration with cross-functional teams, fostering creativity and driving advancements in AI applications

Sammlung und Organisation der technischen und agronomischen Daten unserer Feldsysteme (remote, secure VPN), Pflege und Konfiguration von IOT-Datenbanken und Data Lakes, Erstellung von Datenanalysetool für technische und agronomische Versuche, Eigenständige Durchführung von Routine- und Spezialanalysen (Statistik, Agrar, Marketing, Technik), Visualisierung von Daten, Fachliche Unterstützung der angegliederten Abteilungen