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Design and maintain cloud-based data pipelines and the enterprise Master Data Repository in Azure, enabling reliable data flows and analytics for a high-tech electronics manufacturer.
Designs and builds scalable data pipelines on Databricks and Azure Data Factory, ensuring high-quality data ingestion and governance for a leading data-solutions company.
Build and own the data infrastructure for a risk-detection platform serving e-commerce and logistics, designing pipelines, warehouses, and MLOps to process millions of real-time events.
Build and improve a data foundation for a large retail client, implementing data products, pipelines, and governance to enable a data mesh architecture using GCP, SQL, Python, and dbt.
Design, build, and optimize modern data platforms on Microsoft Azure for leading Dutch organizations, focusing on data ingestion, transformation, modeling, and visualization.
Build and maintain scalable data pipelines in Java, ensuring high-quality, analytics-ready data for cross-functional teams using Hadoop/Spark and cloud platforms.
Build and maintain a cloud-hosted dashboard for water-quality monitoring and algae-control systems, using Python backends, REST APIs, relational databases, and AWS infrastructure.
Build and scale data platforms for heat-asset monitoring and optimization in Eneco’s push to become CO₂-neutral by 2035, using Python, Snowflake, PostgreSQL, and Kubernetes.
Build full-stack Android predictive-maintenance features using Kotlin, Compose, and ML models (TensorFlow/PyTorch) to detect hardware/software issues before they occur, spanning device kernel to cloud.
Build and maintain scalable Python web apps and REST APIs using Django/FastAPI, deploy on Azure, and optimize databases and CI/CD pipelines.
Builds scalable, cloud-native Java services for a risk-compliance platform used by financial institutions, focusing on distributed systems and real-time analytics.
Builds and maintains Java-based data pipelines and ETL processes, working with big data tools like Spark and cloud platforms to ensure data quality and performance.
Build and maintain Python-based data pipelines and services using Kafka, Hadoop, Docker, and Kubernetes, ensuring scalable ingestion, processing, and secure storage for production systems.
Build and maintain data pipelines in Python to ensure high-quality B2B lead data for a fast-growing marketing platform.
Build and maintain scalable RESTful APIs and data pipelines in Python using Django/FastAPI, Azure/AWS, and pytest, handling both relational and NoSQL databases.
Builds and maintains scalable data pipelines using Lakehouse/Lakeflow architectures, job orchestration, and ETL/ELT workflows to support analytics and reporting.
Backend internship integrating telematics data sources and optimizing distributed ingestion infrastructure for a fleet-focused data platform.
Build and scale the backend of an AI runtime platform that autonomously evolves applications, using Go/Kotlin/Java/Rust, PostgreSQL, Redis, and cloud services.
Build and scale the backend platform powering real-time conversational AI for consumer apps, focusing on graph-based AI computations, multimodal services (TTS, LLM, STT), and telemetry-driven personalization.
Principal Data Engineer designs and maintains self-hosted data infrastructure (PostgreSQL, MongoDB) and ETL pipelines for a game studio, ensuring scalability, security, and compliance.
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