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Build and maintain data pipelines in Microsoft Fabric, model insurance-brokerage data, and deliver clean datasets for BI, analytics, and regulatory reporting.
Builds and maintains data pipelines, warehouses, and lakes to integrate industrial systems (ERP, MES, IoT) and enable reporting, BI, and AI projects.
Build and maintain data pipelines, warehouses, and BI dashboards to turn raw data into reliable insights for business decisions using Python, PostgreSQL, and Power BI.
Build and optimize large-scale data pipelines and distributed systems using Spark, Databricks, and AWS to power AI-driven B2B intelligence products.
Build and maintain data pipelines, warehouses, and BI dashboards to turn raw data into actionable insights for public and private sector clients.
Build and optimize ETL/ELT pipelines on Databricks to process and transform data for business teams.
Builds and maintains Python-based ETL/ELT pipelines on GCP and Snowflake to ensure reliable, high-performance data flows for analytics and reporting.
Build and maintain robust data pipelines for a retail client, using Python, SQL, dbt, and GCP services like BigQuery and Airflow to ensure high-quality, production-ready data for BI and AI teams.
Build and maintain scalable big-data pipelines using Spark, Python/Scala, and Hadoop; collaborate with data scientists to deliver robust ETL/ELT workflows.
Build and maintain robust data pipelines and platforms for clients, focusing on reliability, scalability, and observability using tools like Spark, Kafka, and NiFi.
Build and optimize data pipelines and infrastructure for Owkin K, an AI co-pilot that uses agentic AI to accelerate biomedical research and drug discovery.
Builds and optimizes data pipelines in Snowflake and DBT, ensuring clean, governed data for analytics and reporting across the company.
Design and implement scalable cloud data pipelines and warehouses for clients using GCP/AWS/Azure, BigQuery, Spark, and DevOps practices.
Design and build scalable data pipelines and architectures (Data Lake/Warehouse) for industrial clients using Python, SQL, and cloud/Big Data tools.
Build and maintain data pipelines for a strategic data warehouse in the energy sector using Python, Airflow, and SQL.
Build and maintain scalable data pipelines and curated datasets in Snowflake using dbt, Airflow, and Terraform to power CRM analytics and insights for Zendesk’s support operations.
Build and maintain data pipelines, clean and transform raw data, and write SQL queries for analysis while collaborating with analysts and developers.
Build and maintain scalable Big Data pipelines in AWS/Google Cloud/Microsoft Fabric, implement DevOps and IaC, and adopt modern data architectures like Lakehouses and GenAI integrations.
Build and maintain data pipelines in Python and SQL, orchestrating ETL/ELT workflows with Apache Airflow on AWS to feed AI models and internal analytics tools.
Design and build scalable data pipelines and GenAI platforms on AWS, using Spark and modern data architecture to support analytics, AI, and Generative AI use cases.
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