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Lead or Senior Data Engineer building and scaling robust Big Data pipelines in a modern Cloud environment for a data-driven product company.
Build and optimize scalable data pipelines and lakehouse infrastructure (Spark, Databricks, Snowflake) for a SaaS data-management platform, with early AI use-case enablement.
Build and maintain Wavo’s data platform, designing robust pipelines to ingest external data, model datasets, and power real-time risk and financing decisions for a fast-growing fintech.
Build and maintain scalable data pipelines in Python/PySpark on AWS to ingest, transform, and expose market data for anomaly detection in a fintech setting.
Build and maintain data analytics pipelines, models, and dashboards on Google Cloud, ensuring quality and collaborating in an agile team.
Build and maintain data pipelines for media analytics, sourcing from APIs and BigQuery, transforming data for performance dashboards, and optimizing GCP deployments.
Build and maintain scalable data pipelines and ETL processes to power AI model training and analytics at a cutting-edge AI company.
Builds and maintains data pipelines, transforms raw data into analytics-ready datasets, and creates dashboards for reporting using Python/Scala, SQL, and NoSQL databases on AWS.
Analyze user behavior and product metrics to build dashboards and reports that guide decisions using SQL, Python, and visualization tools.
Designs and maintains scalable data pipelines and infrastructure using Python, SQL, Spark, and cloud platforms to enable analytics and AI workloads.
Senior data engineer leading the migration of legacy data systems to a modern architecture, building robust pipelines, and ensuring data quality and governance for business impact.
Design and build scalable data pipelines, ETL/ELT workflows, and cloud data architectures for clients, ensuring data quality and security while enabling real-time analytics and digital transformation.
Build and maintain large-scale geospatial data pipelines on Kubernetes, integrating remote sensing and AI for energy-market intelligence.
Build and maintain data pipelines, storage systems, and CI/CD workflows for a large banking group, using Python, SQL, Spark, Kafka, and cloud storage.
Build and maintain data pipelines for a migration to SAYVINT, using Airflow and Dataiku on AWS to integrate and cleanse application data.
Build and maintain scalable data pipelines in BigQuery and dbt, transforming raw retail-media data into clean, reusable layers for analytics and AI teams across Europe.
Build and own a scalable Azure-based data lake and analytics platform, from ETL pipelines to embedded BI and future ML/GenAI exploration for industrial clients.
Build and maintain scalable data pipelines using Python, Spark, and Databricks to support analytics and AI projects for clients across industries, with a focus on sustainability.
Build and maintain data pipelines for a healthcare AI project, ingesting and harmonizing diverse medical datasets to support deep-learning model training and deployment.
Build and maintain Wizaly’s data pipelines and attribution algorithms using Scala/Spark and SQL, ensuring accurate, real-time marketing performance insights for clients.
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