Data & Analytics Lead Consultant

Job description

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 Description Leads complex data and analytics initiatives that deliver trusted, secure, and scalable enterprise data capabilities. This role combines technical expertise, business knowledge, and cross-functional leadership to advance data strategy, governance, quality, modernization, and analytics across the enterprise. The Data & Analytics Lead Consultant provides strategic and technical leadership to deliver enterprise data and analytics capabilities that support business goals and enable trusted, data-driven decision making. This role combines strong data and analytics expertise with business knowledge to lead complex initiatives, establish scalable solutions and standards, and foster collaboration across business, technology, analytics, and data governance teams. The Lead Consultant will contribute to enterprise data strategy across Data Governance & Quality, Analytics Ecosystem, Master Data Management, and Data Information & Security, while helping modernize data platforms and engineering practices. Key Responsibilities: Data & Analytics Strategy Lead complex, cross-functional data and analytics initiatives aligned with business and enterprise data strategies. Translate business objectives and requirements into actionable data, analytics, and technology solutions. Contribute to functional strategy and define measurable success criteria for enterprise-wide initiatives. Develop frameworks, roadmaps, and implementation plans for strategic data initiatives. Identify opportunities to improve how enterprise data is collected, integrated, managed, governed, and consumed. Evaluate technical approaches and communicate recommendations, risks, dependencies, and trade-offs to stakeholders. Data Solutions & Modernization Lead the design and delivery of scalable data solutions supporting analytical and business needs. Provide technical guidance for data ingestion, transformation, integration, storage, modeling, and consumption. Apply modern data technologies and frameworks such as Microsoft Fabric, Azure, Spark, Python/PySpark, SQL, Kafka, dbt, or equivalent technologies. Support modernization of legacy data platforms and processes through cloud, lakehouse, and modern data engineering patterns. Promote reusable patterns and standards that improve consistency, scalability, maintainability, and delivery efficiency. Ensure data solutions meet expectations for performance, reliability, security, and operational support. Data Governance & Quality Establish and promote practices that improve the reliability, consistency, accessibility, and quality of enterprise data. Partner with business and technology teams to define data ownership, stewardship, metadata, lineage, and quality expectations. Define and monitor data quality controls, validation rules, reconciliation processes, and key data quality metrics. Help establish trusted and governed data sources that can be consistently reused across business and analytical applications. Ensure appropriate data lifecycle, retention, privacy, and governance requirements are incorporated into data solutions. Analytics Ecosystem Enable the appropriate data, platforms, and capabilities needed to conduct analysis, inform decisions, and deliver measurable business outcomes. Partner with analytics, data engineering, product, and business teams to develop integrated analytical capabilities. Promote consistent and reusable approaches to data modeling, data access, semantic definition, and analytics consumption. Identify gaps in the analytics ecosystem and recommend improvements to platforms, processes, and capabilities. Master Data Management Support the development and adoption of trusted enterprise sources for key business data. Establish processes and standards that improve the consistency, quality, and usability of shared data across business domains. Partner with data owners, data custodians, technology teams, and business stakeholders to establish authoritative data definitions and ownership. Identify opportunities to reduce duplication and inconsistencies across enterprise data assets. Data Information & Security Partner with security, governance, and technology teams to protect enterprise data through appropriate security and access controls. Ensure data solutions align with applicable privacy, security, regulatory, and enterprise requirements. Incorporate security considerations into data architecture, integration, storage, and consumption designs. Promote appropriate data classification and access practices throughout the data lifecycle. Program Leadership & Enterprise Collaboration Lead integrated, multi-track projects and initiatives, ensuring high-quality deliverables, effective risk management, and alignment with business objectives. Develop project plans, establish controls, track performance, manage dependencies, and communicate risks and status. Collaborate across business units and technology teams to resolve complex data and delivery challenges. Foster enterprise-wide collaboration and alignment across data engineering, analytics, architecture, governance, security, and business teams. Translate complex technical concepts into clear business implications, capabilities, risks, and recommendations. Communicate effectively with technical teams, business stakeholders, and senior leadership. Mentor team members and promote knowledge sharing and adoption of data and analytics best practices. Core Skills Strong knowledge of data and analytics strategy, data governance, data quality, data engineering, and analytics. Strong understanding of modern data architecture, including lakehouse, data warehouse, batch and streaming processing, and cloud-based data platforms. Hands-on knowledge of technologies such as Microsoft Fabric, Azure, Spark, Python/PySpark, SQL, or equivalent modern data technologies. Experience with data modeling, data integration, metadata, lineage, master data management, and data quality practices. Understanding of Microsoft Fabric capabilities including Lakehouse, Warehouse, OneLake, Notebooks, Data Pipelines, and Semantic Models. Experience with modern software and data engineering practices including Git, automated testing, CI/CD, monitoring, and observability. Strong analytical and problem-solving skills with the ability to work through complex technical and business problems. Demonstrated ability to lead complex cross-functional initiatives and influence outcomes without direct authority. Ability to translate technical concepts into business capabilities and communicate effectively with senior stakeholders. Strong collaboration, facilitation, consulting, and written and verbal communication skills. Preferred Skills Experience with Microsoft Fabric and Azure data services. Experience modernizing legacy data platforms and processes into cloud or lakehouse architectures. Knowledge of enterprise data governance, data catalogs, metadata management, lineage, and data quality frameworks. Experience with Master Data Management and enterprise data standardization. Experience with data security, privacy, classification, and access-control practices. Familiarity with generative and agentic AI capabilities and their application to data, analytics, and engineering productivity. Experience 7 or more years of relevant experience in data, analytics, technology, consulting, or related disciplines (Preferred). Experience leading complex or cross-functional data and analytics initiatives (Preferred). #LI-TE1 Skills Analytics, Analytics Strategy, Data Analysis, Data Strategies, E