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Designs and builds scalable Snowflake-based data pipelines and ETL/ELT processes in Azure, using Snowpark and SQL to deliver cloud-native analytics solutions.
Build and optimize cloud data pipelines using GCP services like Dataflow, BigQuery, and Dataproc to help clients process and analyze large datasets efficiently.
Designs and maintains ETL pipelines in Python to ingest and clean biosimilar R&D data from Excel, PDF, and databases, ensuring traceability and reliability for scientists.
Lead Data Engineer defines data processes, ensures end-to-end traceability, and bridges business needs with technical teams in a banking client, using Airflow, PySpark, Kafka, and AWS.
Build and maintain data pipelines in Snowflake and Databricks, ensuring reliable data integrations and product telemetry for a global software company.
Build and maintain data pipelines in Python and SQL to feed business analytics, create Power BI dashboards, and explore AI use cases for a brewery group.
Design and maintain Snowflake-based data pipelines and warehouses that feed credit risk models, analytics, and reporting for a global distributor’s credit function.
Build and optimize modern data pipelines using SQL, scripting, and cloud warehouses (BigQuery/Snowflake) to migrate legacy ETL/ELT systems and ensure scalable, high-quality data architecture.
Design and scale high-performance ELT pipelines for a centralized data warehouse, collaborating with cross-functional teams to build a robust, scalable data platform.
Designs and maintains cloud-based data pipelines and warehouses using Python, PySpark, SQL, and Databricks to power analytics and ML on Microsoft Azure.
Build and maintain cloud-based data pipelines for a global fintech firm, using Python, Spark, and Azure to enable analytics and reporting.
Build and optimize Azure-based data pipelines and architectures, implementing scalable cloud solutions with Azure Data Factory, Databricks, and PySpark.
Design and build graph data models and ETL pipelines for a Metalake platform, using Neo4j, Cypher, and Graph ML to power organizational intelligence.
Lead a data engineering team to build scalable analytics pipelines and models for a large European automotive marketplace, using Python, Spark, Airflow, and SQL.
Build and optimize ETL pipelines, implement data quality checks, and support data infrastructure for A/B testing and statistical analysis at Procter & Gamble in Madrid.
Build and maintain cloud-based data pipelines using Azure services (Fabric, Synapse, Data Lake) and Python/SQL to turn raw data into insights for clients.
Build and maintain scalable cloud data pipelines for a global travel platform, using AWS, Kafka, Spark, and Scala to process billions of daily events.
Build and maintain scalable data pipelines and production-grade ML systems for clinical trials, pharmacovigilance, and drug manufacturing at a global pharma company.
Designs and builds data ingestion pipelines, lakehouses, and datasets in Microsoft Fabric and Power BI, using Spark notebooks and DevOps best practices.
Build and optimize Azure-based data pipelines and cloud architectures, using Azure Data Factory, Synapse, PySpark, and Medallion/Lakehouse patterns to move and transform data securely and at scale.
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