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Senior Data Engineer Con II

Allstate · Remoto Brasil · Flexible / Remote

Publicada em 24/07/2026

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At Allstate, great things happen when our people work together to protect families and their belongings from life's uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers' evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.Job DescriptionJob DescriptionAllstate's Data & Analytics Technology organization is seeking a Data Engineer to design, build, and operate scalable, reliable, and high performing data pipelines that support enterprise analytics, reporting, and advanced data use cases. In this role, you will focus on building robust batch and streaming data solutions using Apache Spark and modern cloud data platforms. Experience with Microsoft Fabric is a strong plus.You will work closely with analytics engineers, data scientists, product teams, and platform partners to transform raw, complex data into trusted, analytics ready datasets. This role plays a critical part in enabling data driven decision making by ensuring data quality, performance, scalability, and operational excellence across the data platform.Key ResponsibilitiesDesign, build, and maintain scalable batch and streaming data pipelines using Apache Spark and cloud-native data technologies.Develop and optimize ETL/ELT workflows to ingest, transform, and curate data from diverse source systems into analytics-ready datasets.Implement data modeling and transformation logic to support reporting, dashboards, and downstream analytical and machine learning workloads.Build and manage data processing workloads within modern lakehouse platforms, including Microsoft Fabric / OneLake (preferred).Ensure data quality, reliability, and consistency by implementing validation checks, monitoring, and reconciliation processes.Optimize Spark jobs for performance, cost efficiency, and scalability across large and comple
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