Design, build, and optimize scalable ETL pipelines and complex data models to support product insights for gaming and eSports. Act as a technical lead by mentoring other engineers and shaping infrastructure and organizational best practices.
Remote Data Engineer Jobs (2,551)
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Staff Data Engineer (Technology Strategy and Ops - Gaming Industry)
Opinov8
·
Full Time
·
1 Week Ago
Opinov8
Design, build, and optimize scalable ETL pipelines and data models to track technology costs and usage. Collaborate with finance and strategy teams to automate workflows and provide technical leadership through mentoring junior engineers.
The Founding Data Engineer will build and own the company's data foundations, including the warehouse, core models, and event taxonomy. They will deliver trusted data products to power analytics, AI features, and in-app reporting for various internal teams.
Design, develop, and maintain end-to-end ETL/ELT pipelines and large-scale data processing frameworks using Python and PySpark. Architect and manage secure, cost-efficient data solutions and warehouse models within the GCP ecosystem.
Implement data pipelines and integrate source systems for data analytics, machine learning, and AI use cases. Responsible for data quality assessment, API deployment, and supporting the project team with CI/CD and Infrastructure-as-Code.
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Design, build, and maintain scalable data pipelines and cloud-based architectures to support federal government programs. Collaborate with cross-functional teams to integrate diverse data sources and ensure compliance with federal security requirements.
Design, develop, and maintain modern data pipelines and Lakehouse architectures using Azure Databricks and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate multiple data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while maintaining and evolving existing SQL Server-based data warehouse environments.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate multiple data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design, develop, and maintain modern data pipelines and Lakehouse architectures using Azure Databricks and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
New "Data Engineer" jobs delivered to your inbox
We'll notify you when new positions matching this search are posted
Design, develop, and maintain modern data pipelines and Lakehouse architectures using Azure Databricks and Azure Data Factory. Integrate diverse data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Design and develop modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, and Azure Data Factory. Integrate multiple data sources into analytical layers while evolving existing SQL Server environments toward scalable cloud architectures.
Own the end-to-end delivery of AI-automated data platform migration projects, including scoping, planning, and execution. Serve as the primary customer contact to manage expectations and partner with engineering teams to refine the delivery playbook.
Own the complete product lifecycle from discovery and ideation to production deployment of AI-native systems. Design and build RAG pipelines, agentic workflows, and full-stack applications using Python and Next.js.
Architect and implement enterprise-grade GenAI data platforms for the ingestion and processing of structured and unstructured data. Design scalable pipelines for knowledge preparation, including chunking, embeddings, and serving AI-ready data models.
Design, build, and maintain scalable data pipelines and cloud-based architectures to support federal government programs. Collaborate with cross-functional teams to integrate diverse data sources and ensure compliance with federal security requirements.
The Senior Data Engineer designs and owns end-to-end data pipelines and the semantic layer using dbt, Airflow, and Snowflake. They set engineering standards, manage Terraform-based infrastructure, and partner with senior stakeholders to translate business needs into durable data products.
Build and ship production-quality data normalization, schema mapping, and enrichment pipelines for a new AI-native data warehouse. Collaborate with Staff engineers on technical architecture and implement AI-accelerated development practices.
Design and implement the core components of an AI-native data platform, including schema mapping, normalization, and distribution pipelines. Establish technical standards and build evaluation infrastructure to ensure the reliability of AI-generated financial data.
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Design and maintain scalable data pipelines using Python, Spark, and Airflow to support entity resolution engines. Collaborate with AI/ML teams to implement AI-native products and optimize complex ETL processes.
Deliver scalable data pipelines, transformation layers, and infrastructure for various client engagements. Mentor other data engineers and contribute to internal standards, proposals, and the wider data community.
The role involves creating and maintaining ETL/ELT data flows using Python and SQL. Responsibilities include optimizing queries for data cleaning, monitoring data pipelines, and supporting other teams with data analysis.
Architect complex systems and make critical technical decisions while mentoring engineers and promoting engineering excellence across teams. Align technical strategies with business goals through cross-functional collaboration and contribute to technical roadmaps and strategic planning.
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