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Senior Data Engineer designing and maintaining scalable batch/streaming data pipelines on AWS, using Python, Spark, Glue, Athena, and Terraform for a data-solutions tech company.
Designs and builds scalable data pipelines and ETL processes using Python, Airflow, and SQL to ingest, transform, and validate data for downstream systems.
Data Engineer building and optimizing large-scale data pipelines using Scala/Python/PySpark on Spark and Databricks, designing Data Lakehouse architectures primarily on Azure Cloud for a consulting firm serving diverse sectors.
Data Engineer building AWS-based data solutions for client projects, focusing on data ingestion, batch/streaming processing, and pipeline orchestration using Python, Spark/PySpark, SQL, Airflow, and a broad set of AWS services.
Designs and maintains scalable AWS-based data pipelines (batch/streaming) while translating business needs into efficient architectures, ensuring data quality, observability, and cost optimization in a tech-driven company.
Data Engineer (Junior/Senior) designing and building GCP data pipelines and ETLs (Python, SQL, BigQuery, Airflow) for a SaaS data & AI product in the real estate sector.
Senior Data Engineer building and maintaining ETL/ELT pipelines, ingesting structured and unstructured data, and developing RAG systems using Python, SQL, Kubernetes, and cloud platforms (Azure/AWS) for an AI Fintech in Madrid.
Lead Data Engineer designing and overseeing end-to-end data pipelines (ingestion, transformation, loading) on the SimpliFi Data Pool—an Azure-hosted repository using Data Vault 2.0, Databricks, Azure Synapse, Informatica IDMC, and PowerBI—while mentoring engineers and enforcing data governance.
Data Engineer designing and maintaining scalable data pipelines on Snowflake and Azure, using dbt, Airflow, Python, Docker, and Kubernetes for a large multinational IT services company.
Data Engineer focused on Palantir Foundry/AIP, building robust data pipelines with Python and PySpark, designing ontologies, and orchestrating solutions on AWS/Azure.
Builds and maintains cloud-based data platforms on Azure, focusing on data ingestion, processing pipelines, and real-time analytics to support client transformation projects.
Designs, builds, and optimizes scalable data pipelines and analytics platforms on Snowflake/Azure, using dbt, Airflow, Python, and Kubernetes for cloud-native data solutions.
Builds and maintains scalable data pipelines, migrates Hadoop to cloud infrastructure, and ensures data quality/availability using Scala, Spark, and cloud tools like Kubernetes and Airflow.
Design and build data pipelines using PySpark, Python, SQL, and AWS, with Airflow for orchestration and Docker/ECS as needed, as part of a 12-month contract in Madrid.
Builds and maintains scalable data pipelines to extract, transform, and integrate data from multiple sources into a cloud data warehouse, ensuring reliability and performance for analytics and decision-making.
Senior Data Engineer at PepsiCo building global data products and pipelines using cloud platforms (AWS, Azure, Snowflake), big data tools (Hadoop, PySpark), and orchestration (Airflow, DBT) to power digital commerce capabilities for the food & beverage giant.
AI Data Engineer at a Spanish tech consultancy, designing and deploying AI solution architectures, building ETL pipelines in Python, designing APIs, and deploying to the cloud using LangChain, FastAPI, vector databases, and Docker.
Senior Data Engineer working on a high-impact tech project in Madrid, designing and maintaining scalable data pipelines using Databricks, Spark, Airflow, Iceberg, and Cloud technologies.
Data Engineer focused on Data Products, operationalizing AI/ML models into reliable, scalable data products (batch and real-time pipelines) using Python, SQL, Spark, Databricks, Airflow, and GCP/Azure within a leading European retail company's Digital Hub in Barcelona.
Data Engineer building data pipelines and systems on a Life Science project using Azure Cloud, Databricks, Spark, Scala, Airflow, Python, and SQL.
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