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Designs and maintains enterprise data architecture, builds scalable data platforms (lakes, warehouses, streaming), and sets standards for modeling, governance, and cloud integration to support analytics and AI.
Designs and builds scalable data pipelines and modern data warehouses using Microsoft Fabric, Azure, and Databricks to enable analytics and reporting.
Designs and builds end-to-end data pipelines on Azure Synapse and Data Factory, modeling relational/ dimensional data in T-SQL and PySpark to deliver governed analytics platforms for clients in industrial, energy, consumer and public sectors.
Build and maintain a Lakehouse platform using PySpark and Databricks, designing Delta Lake schemas and batch/near-real-time pipelines for reliable, scalable data solutions.
Build and maintain a PySpark and Delta Lake-based data lakehouse on Databricks, creating batch and near-real-time pipelines to power analytics and data sharing for internal products.
Build and maintain scalable ETL/ELT pipelines and lakehouse architectures, design data models, and collaborate with ML/AI teams in a hybrid setup.
Build and maintain Snowflake and AWS-based data platforms, automate infrastructure with Terraform, and run secure data pipelines across 60+ countries.
Own and evolve a Snowflake-based data platform and AWS lakehouse, managing Terraform infrastructure and Airflow workflows in a hybrid Barcelona role.
Designs and builds data pipelines and warehouses for clients using Hadoop, Snowflake, Kafka, Spark, and Python/Java.
Builds and maintains data ingestion pipelines, lakehouse environments, and real-time streaming systems using SQL, PySpark, Kafka/Kinesis, and Go.
Builds and maintains PySpark pipelines on Databricks to power reliable data products and enrichment workflows for a SaaS analytics platform.
Design and build scalable data platforms, lakehouse architectures, and ETL/ELT pipelines while collaborating with ML/AI teams to shape data strategy.
Build streaming and batch data pipelines, govern schemas, and create self-serve tools for trading, wealth, and product teams using a lakehouse and time-series layer.
Senior Data Engineer builds scalable data pipelines and a Lakehouse architecture for payments and marketing analytics across AWS and GCP using Dagster/Airflow and IaC.
Design and build Azure Databricks-based Lakehouse pipelines and Spark ETL workflows to power analytics and BI, optimizing cloud data architectures for scale and quality.
Build and maintain a modern data architecture using AWS, SQL, Python, and Power BI to support AI, marketing, development, and cybersecurity projects.
Designs and automates data pipelines, manages APIs, and ensures FAIR data governance to turn scientific data into strategic assets for global R&D decisions.
Design and maintain scalable data pipelines on Databricks using Spark, Delta Lake, and Unity Catalog, collaborating with data scientists and architects to deliver production-grade solutions.
Builds scalable data pipelines using Databricks, Spark, Airflow and Iceberg to power modern lakehouse platforms for Atos’ digital-transformation projects.
Builds ETL pipelines and manages a lakehouse to turn scientific data into FAIR-compliant assets, supporting global R&D decisions at a climate-focused agri-tech company.
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