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Data Engineer with Databricks (Aviation Industry)
Build and maintain big-data pipelines for European air-traffic systems using Databricks, Spark, Kafka, and Oracle; ensure data quality and industrialize production environments.
Remote Mid-Level Data Engineer: Azure/GCP, ETL Pipelines
Designs and builds cloud-based ETL pipelines and data infrastructure on Azure/GCP, working on migrations and modernizations for global clients.
Junior Data Engineer — Python, Big Data, Hybrid
Builds and maintains large-scale data pipelines using Hive, Spark, and Kafka for end-to-end data solutions in a global team.
Senior Data Engineer (Fulfillment) @ Tesco Technology
Build and optimize large-scale data pipelines for retail fulfillment, using Spark, Hive, Airflow, and cloud storage to ensure reliable order and delivery data flows.
Senior Data Engineer (Hadoop, Spark)
Build and maintain scalable data pipelines using PySpark, Hadoop, and Airflow in a DevOps environment, collaborating with analysts and engineers to deliver high-performance solutions.
Data Engineer - Automation & Innovation Department
Build and maintain scalable data pipelines, automate ingestion from Kafka, MQ, SFTP, and databases, and optimize cloud-based processing in GCP using BigQuery, Dataflow, and Airflow.
Data Engineer Technical Leader (Relocation to Poland)
Lead a team to design and build scalable data pipelines and platforms using Spark/Scala, Airflow, and cloud tools, while mentoring engineers and ensuring regulatory compliance.
Senior Data Engineer (Hadoop, Spark)
Build and maintain scalable data pipelines using PySpark, Hadoop, and Airflow in a hybrid Agile environment, collaborating with analysts and engineers to deliver high-performance ETL solutions.
Big Data Engineer with Java
Design and build scalable data ingestion pipelines and microservices using Java, Spark, and Spring Boot, while maintaining big data infrastructure on Cloudera and GCP.
Senior Data Engineer On-Premises Data Platforms
Design and build enterprise-scale on-premises and hybrid data pipelines, ETL/ELT processes, and data warehouses using SQL, Spark, Hadoop, and orchestration tools.
Senior Data Engineer On-Premises Data Platforms
Design and maintain enterprise-scale on-premises and hybrid data pipelines, ETL/ELT processes, and data warehouses using SQL, Spark, Hadoop, and orchestration tools.
Senior Data Engineer (Knowledge graph)
Build and optimize scalable data pipelines, ETL processes, and cloud-based data architectures using Python/Java/Scala, Spark, Kafka, and cloud platforms (AWS/Azure) to support large-scale analytics and client projects.
Big Data Engineer with Java
Build and maintain scalable data ingestion pipelines and microservices using Java, Spark, and Spring Boot to power ING’s data lake for analytics and compliance.
Machine Learning Engineer/Data Engineer
Build and deploy ML models and data pipelines for a financial-services client’s new AI practice using Python, Spark, Hadoop, and SQL.
Senior Data Engineer/ML
Senior Data Engineer/ML Engineer builds and deploys ML models and pipelines in a banking context using Python, Spark, and AWS services like SageMaker and Glue.
Senior Big Data Engineer
Senior Big Data Engineer builds and maintains scalable data pipelines using Scala, Spark, and Kubernetes to process large datasets and generate reports for enterprise clients.
Senior Data Engineer (Fulfillment)
Senior Data Engineer builds and scales data pipelines for Tesco’s fulfillment operations, optimizing order and delivery data to improve logistics and delivery efficiency using Spark, Hive, and Airflow.
Senior Big Data Engineer
Builds and optimizes large-scale data pipelines using Scala, Spark, Kafka, and Airflow to process massive datasets for global stakeholders in the mobile app ecosystem.
Copy of Data Engineer
Build and maintain scalable ETL pipelines and data models using Spark, Scala, and cloud storage, then deliver clean data to analytics teams via BI tools like Databricks and Power BI.
Data Engineer Intern
Data Engineer Intern builds and enhances data models and predictive systems using SQL, Python, Spark, and cloud platforms like Azure, while collaborating with data teams and business stakeholders.