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Senior Data Engineer designing and building scalable data pipelines on Microsoft Fabric and Azure, using Python/PySpark, SQL, and Power BI to deliver analytics solutions for enterprise clients.
Design scalable data architectures and MLOps pipelines, industrialize ML models, and ensure data quality for enterprise AI projects in cloud environments.
Build and maintain robust data pipelines and cloud data platforms, preparing data for AI systems and ensuring quality and governance for analytics and agentic use cases.
Designs, builds, and maintains scalable data pipelines and architectures to integrate and process diverse data sources for analytics and decision-making.
Design, build, and optimize scalable data pipelines and architectures for international clients, ensuring data quality, security, and performance while collaborating with data scientists and business teams.
Build and optimize data pipelines using Python, SQL, FastAPI, and Google Cloud tools, while experimenting with AI to enhance workflows and drive business impact.
Build and maintain scalable data pipelines and warehouses in GCP/Azure, transforming raw data into clean, reliable insights for business decisions and dashboards.
Build and maintain data pipelines and warehouses (Snowflake, dbt, Python) to turn raw business data into analytics-ready models that power reporting and decision-making.
Build and optimize robust, scalable data pipelines and architectures for enterprise clients, blending hands-on engineering with consulting to turn business needs into actionable data solutions.
Designs, builds, and maintains scalable data pipelines and cloud-based data platforms for financial clients, using AWS, Databricks, Airflow, dbt, PySpark, and CI/CD.
Lead a team of Data Engineers to design and deliver cloud-native data platforms and AI solutions for global clients, using Python, Spark, Airflow, and major cloud platforms (GCP, AWS, Azure).
Build and maintain scalable data pipelines and warehouses for an online DIY/home-improvement marketplace using Snowflake, Airflow, Python, Kafka, and S3.
Build and maintain ETL/ELT pipelines, ensure data reliability, and automate data ingestion from SFTP/APIs/databases into a Datalake using Python, Airflow, AWS, and BigQuery.
Build and maintain robust data pipelines and ETL workflows for a public-sector client using Talend/Informatica, Airflow, and modern data platforms like Trino and Iceberg.
Build, optimize, and harden GCP-based data pipelines for a large food-sector group, using BigQuery, Airflow, DBT, and Cloud Storage while enforcing data quality and agile practices.
Build and maintain robust data pipelines on Google Cloud for analytics and AI clients, using BigQuery, Dataflow, Pub/Sub, and Airflow to process batch and streaming data at scale.
Build and optimize Snowflake-based data pipelines and warehouses, migrating legacy systems to modern cloud platforms while ensuring performance, scalability, and cost efficiency.
Design and build scalable Snowflake-based ELT pipelines, optimize SQL workloads, and model data warehouses for modern data platforms.
Builds and maintains scalable data pipelines for an AI platform that processes satellite imagery, using Python, Airflow, PostgreSQL/PostGIS, and AWS.
Senior Data Engineer building and maintaining a modern Lakehouse data platform using Databricks, Airflow, and PySpark pipelines with a focus on data governance and lineage.
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