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Build and maintain IBM’s Quantum Software data lake, designing scalable pipelines and orchestration workflows to power analytics and insights for quantum computing.
Build and maintain data pipelines, ETL/ELT workflows, and AI/ML models to support naval in-service support programs using Python, Airflow, and modern data architecture.
Builds and maintains data pipelines and infrastructure to power AI/ML applications, collaborating with data scientists to integrate models into systems.
Builds and maintains a clean data warehouse, designs BI dashboards, and ensures data quality with Python, SQL, Airflow, and Superset.
Builds and maintains global equity datasets for quantitative research, mapping tickers and validating data across markets using Python, SQL, and financial data APIs.
Build and maintain scalable ETL/ELT pipelines and data warehouses using Python, SQL, and cloud platforms to power analytics and AI products.
Build and maintain large-scale data infrastructure for a top crypto exchange, integrating AI agents to automate scheduling, cost optimization, and incident response across distributed systems.
Design and maintain scalable data architectures, ETL pipelines, and data lakes for a national library system using AWS, SQL, Python, and Airflow.
Build and productionize generative AI models and LLM-driven applications using PyTorch, RAG pipelines, and vector databases, while engineering robust MLOps and data pipelines in Python.
Build and maintain a modern enterprise data warehouse using Snowflake, dbt, and Airflow to enable self-service BI and reporting across the organization.
Designs and builds scalable big-data pipelines using Spark and Scala, integrating Python, Airflow, and DevOps practices to process and analyze large datasets.
Designs and optimizes scalable Azure data pipelines using Databricks, ADF, Data Lake Gen2, and SQL, ensuring reliable data flows for an enterprise project.
Build and optimize batch and streaming data pipelines in Java and Apache Beam, orchestrate with Airflow, and ensure robust testing and observability for analytics workloads.
Build and maintain scalable data pipelines on Azure using Databricks, ADF, and PySpark to process and transform enterprise datasets.
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.
Builds and maintains data pipelines and ML workflows in Python, using Airflow/Kubeflow and Docker/Kubernetes on GCP to process large datasets.
Build and scale ELT pipelines for iGaming using Airflow, dbt, Python, and AWS, transforming raw data into reliable datasets for analytics and BI teams.
Build and maintain cloud-based data pipelines and transformations using GCP, Python, SQL, and dbt to turn raw data into business insights for clients.
Designs and implements batch and streaming data pipelines using Apache Beam/Spark/Flink, optimizes jobs for performance and cost, and builds robust tests and observability for analytics and downstream services.
Designs and maintains Snowflake-based data pipelines and ETL workflows using SQL, Python, and orchestration tools like Airflow.
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