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Build and maintain scalable data pipelines for corporate lending using Python, PySpark, and SQL to transform data into analytics and insights.
The Senior Data Engineer will design and develop scalable backend systems, microservices, and data pipelines to support business processes. The role involves building reliable integrations and optimizing data workflows using technologies like C#, Java, Go, or Python.
Senior Data Engineer at The Codest, developing and automating tools for a major European e-commerce platform's Data Platform. Tech stack: Python (primary), JVM languages, GCP (BigQuery, Composer, Dataproc, Cloud Run), SQL, PySpark, Docker.
The Data Engineer will work on migrating and modernizing data and analytics processes using Google BigQuery, Dataform, and Python. The role involves collaborating with teams to improve data pipelines and implement best practices.
The Data Engineer will design and implement batch and streaming data pipelines using Spark and Python. The role involves building ETL/ELT processes and collaborating with data teams to ensure data quality and performance.
Data Engineer working on production data pipelines using Python, PySpark, and SQL within Microsoft Fabric, Azure Synapse, or Databricks environments, handling cloud data in Delta/Parquet formats.
Reppls is looking for a Data Engineer to manage and scale data pipelines and platform infrastructure for quantitative investing workflows. The role involves collaborating with data scientists and engineers using Python, Snowflake, Airflow, dbt, and AWS.
The Senior Data Engineer will design and maintain a core data platform for an asset management firm, focusing on building data pipelines, dbt models, and a FastAPI backend.
Data Engineer building and maintaining scalable ETL/ELT data pipelines for a financial sector client, integrating ERP, HR, and finance systems using Databricks, Snowflake, and Azure Data Platform.
Data Engineer responsible for planning, developing, and maintaining cloud data environments for clients, focusing on Azure Data Factory and Databricks pipelines.
Designs and implements data platforms—data lakes, warehouses, and ETL/ELT pipelines using Python and Spark—for industrial and manufacturing clients, handling the full cycle from requirements gathering through architecture to production deployment.
Data Engineer designing and operating scalable real-time and batch data pipelines and microservices for a cloud-based risk platform, using Java, REST APIs, and DevOps practices.
Builds and maintains data pipelines and warehouses on Azure, using Spark, SQL, and ETL tools to enable analytics and reporting.
Builds and maintains data pipelines, optimizes SQL queries, and ensures data accuracy in Teradata environments for analytics and reporting.
Designs and maintains scalable data pipelines on GCP/BigQuery, collaborating with teams to transform raw data into actionable insights. Core tools: Airflow, Terraform, and CI/CD pipelines.
Senior Data Engineer in Warsaw designing and building end-to-end ELT pipelines from Azure Blob Storage into Snowflake, developing dbt models and Python scripts with a focus on data quality and scalable analytics.
Data Engineer leading the design and delivery of modern data platforms and analytics solutions using Microsoft Fabric, Databricks, and the Azure ecosystem, with a focus on lakehouse architectures and medallion design patterns.
Data Engineer designing and maintaining ETL/ELT data flows, modeling data with dbt and SQL on Google Cloud Platform/BigQuery, and building visualizations with Lightdash/Looker to support Growth, Marketing, Product, and Finance teams.
The Data Engineer will design and optimize scalable cloud data pipelines and manage migrations to Google BigQuery. The role involves collaborating with cross-functional teams to deliver production-ready data products for BI and reporting.
The Data Engineer will design and implement batch and streaming data pipelines within Google Cloud Platform. The role involves integrating systems and collaborating with data analysts and architects using tools like BigQuery, Dataflow, and Airflow.
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