[Hiring] Data Engineer @Allstate

Role Description

Allstate’s Risk Data Team is seeking a Data Engineer to help build the trusted data foundation that enables faster decisions, proactive risk management, and measurable improvements in control effectiveness. This role will focus on transforming fragmented risk and control data into reliable, accessible, and actionable information through scalable data pipelines, governed datasets, analytics-ready data products, and automation.

You will work closely with analytics engineers, product teams, risk partners, platform teams, and governance stakeholders to integrate data from internal platforms, vendor tools, and external sources. The role plays a critical part in reducing reliance on manual reporting and spreadsheets, improving confidence in risk data, and enabling dashboards, reporting, analytics, and future AI-based capabilities across the organization.

Key Responsibilities

• Design, build, and maintain trusted risk data products and scalable batch and streaming data pipelines using cloud-native technologies.
• Integrate data from risk platforms, control systems, vendor tools, operational applications, and external sources into governed, analytics-ready datasets.
• Develop and optimize ETL/ELT workflows that automate data ingestion, transformation, validation, reconciliation, and delivery.
• Build and manage data processing workloads within modern data lake and lakehouse environments, including Microsoft Fabric and OneLake.
• Implement data quality, monitoring, lineage, and reconciliation processes to ensure data reliability, consistency, and accuracy.
• Create curated datasets and analytical outputs that support operational reporting, leadership dashboards, trend analysis, risk identification, and decision-making.
• Optimize data architectures, schemas, and processing patterns for scalability, performance, resilience, and cost efficiency.
• Develop reusable frameworks, engineering standards, CI/CD pipelines, automated testing, and operational monitoring to improve productivity and maintainability.
• Partner with analytics, product, risk, governance, security, and compliance stakeholders to establish data definitions, metrics, quality standards, and secure data access.
• Participate in Agile delivery activities including sprint planning, backlog refinement, design reviews, and continuous improvement initiatives.

Qualifications

• 4+ years of experience as a Data Engineer or in a similar role building and supporting production-grade data pipelines and data products.
• Hands-on experience with Apache Spark for large-scale data processing and transformation.
• Strong proficiency in Python, SQL, and modern data engineering best practices.
• Experience developing ETL/ELT solutions within cloud-based data lake, lakehouse, or analytics platforms.
• Experience designing and optimizing analytical data models that support reporting, dashboards, operational insights, and advanced analytics.
• Strong understanding of data quality, validation, monitoring, reconciliation, and production support processes.
• Experience with CI/CD pipelines, version control, automated testing, and infrastructure-as-code concepts.
• Demonstrated ability to troubleshoot complex data quality, performance, and integration issues across multiple data sources.
• Strong verbal and written communication skills with the ability to explain technical concepts to both technical and non-technical audiences.
• Experience with Microsoft Fabric, OneLake, or similar modern analytics platforms preferred.

Essential Qualifications

• Experience working with risk, controls, compliance, audit, governance, or regulatory data domains.
• Experience building real-time, near real-time, or event-driven data processing solutions.
• Familiarity with data governance, metadata management, data lineage, access controls, and enterprise data quality frameworks.
• Experience with orchestration and workflow management tools for data pipelines.
• Experience supporting operational reporting, executive dashboards, trend analysis, advanced analytics, or machine learning initiatives.
• Interest in building governed, scalable datasets that support AI and data-driven products.

Compensation

Compensation offered for this role is 100,000.00 - 170,500.00 annually and is based on experience and qualifications.