At Allstate

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.

Your role in the team

Allstate's Data & Analytics Technology organization is seeking a highly skilled and hands-on Senior Data Engineer to design, build, optimize, and support scalable, reliable, and high-performing data platforms and pipelines. This role is focused on developing enterprise data solutions that enable analytics, reporting, AI/ML, and data-driven decision making across the organization.

The ideal candidate possesses strong expertise in modern data engineering practices, Apache Spark, cloud-based data platforms, and lakehouse architectures. As a senior individual contributor, the candidate will provide technical leadership, mentor junior engineers, drive engineering best practices, and contribute directly to the design and implementation of enterprise-scale data solutions.

The role requires close collaboration with Analytics Engineers, Data Scientists, Product Managers, Platform Engineers, Security teams, and business stakeholders to deliver trusted, scalable, and analytics-ready data products.

Key responsibilities:

• Design, develop, and maintain scalable batch and streaming data pipelines using Apache Spark, cloud-native technologies, and modern data platforms.
• Build and optimize ETL/ELT frameworks to ingest, transform, validate, and curate data from diverse enterprise source systems.
• Develop and implement data solutions leveraging Microsoft Fabric, OneLake, Lakehouse, Data Warehouse, and Semantic Model capabilities.
• Design data models, storage strategies, and data processing architectures that support enterprise analytics and reporting requirements.
• Partner with Analytics Engineers, BI teams, Data Scientists, and business stakeholders to deliver trusted, reusable, and analytics-ready datasets.
• Drive adoption of engineering best practices including code quality, peer reviews, automated testing, CI/CD, and documentation standards.
• Ensure data quality, governance, lineage, metadata management, security, privacy, and compliance requirements are incorporated into data solutions.
• Monitor and optimize performance, scalability, reliability, and cost efficiency of distributed data processing workloads.
• Develop reusable frameworks, libraries, templates, and engineering patterns that improve development productivity and consistency.
• Implement and support CI/CD pipelines, Infrastructure-as-Code principles, and deployment automation for data engineering solutions.
• Troubleshoot complex data integration, performance, and operational issues across platforms and pipelines.
• Collaborate with platform, infrastructure, architecture, and security teams to build resilient, scalable, and secure enterprise data solutions.
• Create and maintain technical documentation, architecture diagrams, operational runbooks, and implementation guides.
• Participate in technical design sessions, architecture reviews, and engineering discussions while providing recommendations and solutions.
• Mentor and guide junior and mid-level data engineers through technical coaching, code reviews, design reviews, and knowledge sharing.
• Evaluate and adopt emerging technologies, engineering practices, and AI-assisted development capabilities to improve delivery velocity and quality.
• Support production operations through monitoring, root cause analysis, incident resolution, and continuous improvement initiatives.
• Contribute to Agile delivery processes including sprint planning, estimation, backlog refinement, and execution of engineering commitments.
• Promote DataOps practices, automation, observability, and engineering excellence across development teams.
• Serve as a technical subject matter expert for modern data platforms, Spark processing, Microsoft Fabric, and lakehouse architectures.