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Data Engineer processes large audio, text, and image datasets using Python/Rust, Data Lake, and event-driven architectures in a fintech company.
Designs and builds scalable data platforms in cloud and on-premise environments, optimizing pipelines to support data-driven decisions within agile teams.
Builds and maintains AWS-based data pipelines and Spark jobs in a Data Mesh architecture using Python and Agile workflows.
Builds cloud-based data pipelines and RAG architectures that power generative AI and large language models for a Polish AI team.
Build and maintain AI/ML pipelines, deploy models, and manage lifecycle with tools like MLflow, Kubeflow, or cloud platforms (AWS SageMaker/Azure ML).
Builds and maintains ETL/ELT pipelines and data models for a financial-sector B2B platform, using SQL, Python/Scala/Java, and Git.
Build and optimize Google Cloud Platform data pipelines to support T-Mobile’s data transformation strategy using modern cloud-native tools.
Designs and builds cloud data pipelines using Azure Databricks, PySpark, and Delta Lake to power analytics and BI, integrating with SQL Server and Azure Data Factory.
Designs and maintains AWS-based data pipelines and dimensional models to power business analytics and reporting for enterprise clients.
Builds and maintains a large-scale predictive-maintenance pipeline for jet engines using Python, Spark, Databricks, and Azure cloud services.
Build and maintain financial performance frameworks, automate KPI dashboards, and lead budgeting/forecasting for a global consulting firm’s internal analytics team.
Build and maintain scalable data pipelines on Azure using Databricks, ADF, and PySpark to process and transform enterprise datasets.
Senior Snowflake Data Engineer designs Snowflake-based data platforms, builds ETL/ELT pipelines, and implements CI/CD for Azure environments.
Design and build scalable AWS data pipelines that ingest from DynamoDB, Aurora PostgreSQL, and Neptune, then curate them into an Apache Iceberg-based data lake and orchestrate with Airflow.
Build and maintain data pipelines on Databricks, using Python and cloud platforms to transform and automate data workflows.
Build and maintain NLP and ML pipelines using Python and Spark to deliver data solutions for a client project.
Leads a remote team to design and maintain a central data layer, making architecture decisions and mentoring engineers through hands-on coding and workshops.
Build and maintain a Data Packaging Framework to standardize source data into Data Products using GenAI/LLM techniques.
Builds and maintains ETL pipelines using SQL Server Integration Services and T-SQL for data workflows.
Designs and builds scalable AWS-based ETL/ELT pipelines and data models for product margin reporting, ensuring data quality and lineage.
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