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Build and optimize cloud-based data platforms using Databricks, SQL, Python, and cloud data warehouses to enable data-driven decisions for global clients.
Senior Data Engineer builds and maintains cloud-based data platforms using Azure/AWS, SQL, Python, and PySpark to enable data-driven decisions for global clients.
Senior Data Engineer builds cloud-based data platforms (Azure/AWS) using SQL, Python, PySpark, and modern warehousing tools to enable data-driven decisions for global clients.
Design and build scalable data pipelines and warehouses on Google Cloud Platform using BigQuery, SQL, and Python to enable data-driven decisions for global clients.
Senior Data Engineer builds scalable data platforms using cloud tools (Azure, GCP, AWS), Databricks, and modern warehouses (Synapse, BigQuery, Redshift, Snowflake) to enable data-driven decisions.
Builds and maintains scalable data pipelines using PySpark, Databricks, and Azure to process and transform data for reliable business insights.
Senior Data Engineer designs and builds scalable data platforms using cloud tools (Azure/GCP/AWS), Databricks, and modern warehouses (Synapse/BigQuery/Redshift/Snowflake) to enable data-driven decisions.
Build and optimize Python-based ETL/ELT pipelines to migrate on-prem MS SQL Server data into Databricks, ensuring data quality and performance for AI/ML projects.
Builds and optimizes distributed data pipelines using Apache Spark (Python/Scala) to process large datasets for enterprise clients.
Build and scale ad-tech data infrastructure that processes billions of events daily, transforming raw ad server and SSP/DSP data into real-time analytics for campaign optimization and business intelligence.
Senior Data Engineer builds scalable pipelines to process billions of market data records for trading systems using Python, SQL, Spark, and Kafka.
Build and maintain AWS serverless pipelines: Glue ETL, Lambda integrations, DynamoDB writes, API Gateway endpoints, and CI/CD with CDK and Git.
Build and own the data pipelines, ML feature stores, and inference APIs that power renewable-energy analytics at scale, integrating forecasts into a SaaS platform for wind, solar, hydro, and storage assets.
Principal Data Engineer builds and scales Azure-based data pipelines, lakes, and analytics platforms using Databricks, PySpark, and Azure services to deliver governed data solutions for clients.
Build and maintain ETL pipelines, analyze financial data with Python/SQL, and create dashboards in Power BI to support treasury and finance decisions.
Lead the design and build of large-scale data pipelines and platforms using PySpark, GCP, and Airflow to power analytics, AI features, and real-time measurement for a major retail and media company.
Build data pipelines and a knowledge graph to unify messy engineering data (BIM, PDFs, spreadsheets) into a queryable system for AEC firms.
Build and optimize distributed data pipelines using Java, Spark, and Scala for a banking-focused client, handling ETL and big-data workloads.
Build and maintain Java-based risk and valuation applications for derivatives trading, migrating legacy systems to cloud and improving performance for intra-day processing.
Build and maintain Java-based risk and valuation applications for derivatives trading using Spring Boot, Kafka, and cloud infrastructure to support market risk analysis and regulatory compliance.
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