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Build and maintain reliable data pipelines and analytics models to help clients optimize energy contracts and track sustainability progress using SQL, Python, and Databricks.
Build and optimize modern data pipelines and platforms using Spark and Scala for enterprise clients, focusing on scalable ETL, data quality, and cloud-based analytics.
Builds and maintains robust data pipelines for financial institutions, collecting and transforming data for analytics and regulatory compliance using Python, SQL, and cloud platforms.
Designs and builds data pipelines on Google Cloud Platform to improve data access and integration using SQL.
Designs and maintains ETL pipelines and cloud-based data systems using Python/Java and AWS/Azure/GCP to support data science and business needs.
Data Engineer builds and optimizes cloud data pipelines on GCP for clients’ digital transformations, implementing best practices and scalable solutions.
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.
Build and optimize data pipelines using DBT and SQL to create scalable data lakes for clients in a collaborative, client-facing role.
Consultant builds and optimizes Databricks-based data pipelines and architectures for clients, using Python and cluster management.
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.
Leads the design and rollout of a secure, governed data and AI platform for a major media/public-sector client, integrating GCP/AWS, Kubernetes, Kafka, and Collibra with strict data-security and DevOps practices.
Build robust data pipelines and cloud-native platforms using Python, SQL, dbt, Airflow, BigQuery, Snowflake, and Databricks, while integrating AI/ML workflows and RAG systems.
Build and migrate data pipelines using Spark and Databricks, modernizing legacy systems and ensuring data quality for cloud-based platforms.
Designs ETL pipelines and builds data warehouse models for business intelligence in multi-ERP environments using SQL and BI modeling.
Build and optimize data pipelines using SQL, Spark, and cloud platforms like Microsoft Fabric, Databricks, and Azure for clients, while delivering BI solutions with Power BI.
Designs and builds cloud-native data pipelines and platforms using Python, SQL, dbt, Airflow, and GCP, integrating AI/ML workflows and real-time analytics.
Data Engineer to migrate data to SAYVINT, build and maintain Airflow and Dataiku pipelines, and ensure data reliability in a multi-referential environment.
Build and maintain data pipelines in Snowflake for banking clients, focusing on ingestion, transformation, and quality for credit, risk/compliance, and payments systems.
Build and expand cloud-based data platforms (Databricks, Azure, AWS) for BI, AI, and API needs as a junior Data Engineer/Architect at a data/IA consultancy.
Build and maintain scalable cloud/big-data pipelines for clients using PySpark and Airflow on Databricks, following Agile practices and data-governance standards.
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