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Build and optimize cloud data pipelines for clients using Spark, Kafka, and cloud platforms like Databricks or Azure, ensuring scalable, real-time data solutions.
Build and maintain scalable ETL/ELT pipelines on Google Cloud Platform using BigQuery, DBT, and Airflow to deliver clean, documented datasets for analytics and marketing teams.
Build and maintain scalable ETL/ELT pipelines on GCP to feed a modern data platform, enabling analytics and dashboards for an e-commerce and natural-cosmetics retailer.
Senior Data Engineer builds and optimizes cloud-based data pipelines and warehouses using Spark, Kafka, and cloud platforms (Databricks, Snowflake, Azure) to enable real-time analytics and reporting for enterprise clients.
Build and maintain modern GCP-based data pipelines and warehouses for a fast-growing natural-cosmetics e-commerce company.
Build and optimize cloud-based data pipelines for clients in finance, healthcare, logistics, mobility, and climate sectors using Spark, Kafka, and cloud platforms like Azure/GCP.
Build and maintain scalable Azure data pipelines using Azure Data Factory, Synapse, Databricks, and Fabric to transform raw data into actionable insights for BI and analytics teams.
Design and build end-to-end data pipelines on Microsoft Azure, using Data Factory, Data Lake, SQL DB and Analysis Services to deliver production-ready analytics and AI platforms.
Build and maintain AWS-based data pipelines and lakehouse architecture for a large bank’s AI and ESG initiatives, using PySpark, Java, and CI/CD.
Design and lead modern, scalable data platforms on Azure and GCP, build robust pipelines with Spark/Databricks/Dataflow, and mentor teams while optimizing costs and governance.
Builds and maintains robust data pipelines for financial institutions, collecting and transforming data for analytics and regulatory compliance using Python, SQL, and cloud platforms.
Lead a team to build, optimize, and maintain modern data pipelines using DBT and SQL, ensuring reliable, scalable data infrastructure for enterprise clients in finance, healthcare, mobility, and climate sectors.
Data Engineer builds and maintains scalable data pipelines, integrates diverse sources, and ensures data quality for clients and internal projects using Python, Spark, and cloud platforms.
Build and maintain cloud data pipelines and analytics for banking/insurance clients using Spark, Kafka, AWS, and Python, with a focus on data quality and risk/finance reporting.
Designs and runs Azure-based big-data pipelines using Spark, Hadoop, and Azure services to ingest, process, and store data for analytics and AI projects.
Build and maintain scalable data pipelines using DBT and SQL to transform raw data into reliable, analytics-ready models for modern data warehouses.
Design and operate a secure, GDPR-compliant data lake for identity verification and AI research, building storage, APIs, and orchestration tools that handle sensitive biometric and KYC data at scale.
Design and build robust cloud data architectures on Microsoft Fabric and Azure for enterprise clients, from ingestion to production, using Data Vault 2.0, Azure Data Factory, Synapse, and Data Lake.
Designs and builds cloud-based data pipelines and analytics platforms on Microsoft Azure or GCP, using ETL tools, SQL, Python/Java/Scala, and data-lake architectures to deliver business insights.
Build and maintain scalable data pipelines, integrate data into a cloud-based data lake, and collaborate with data scientists to enable predictive modeling using Python, Spark/Databricks, and SQL.
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