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The Data Expert provides technical leadership in designing and delivering scalable, reliable, and well-governed enterprise data solutions. This role defines data architecture and engineering standards, solves complex data challenges, and drives modernization and engineering excellence across teams.

As a Data Expert, you will provide technical leadership in the design, engineering, governance, and evolution of enterprise data solutions. This role serves as a senior technical authority for complex data initiatives, translating business and analytical needs into scalable, secure, reliable, and well-governed data products. You will lead the design of modern data architectures and engineering patterns, establish technical standards and best practices, and solve highly complex data problems spanning multiple systems and business domains. You will partner with product managers, architects, engineers, analytics teams, and business stakeholders to modernize data platforms and create trusted, reusable data capabilities. You are expected to operate with a high degree of autonomy, influence technical direction across teams, mentor engineers, and drive continuous improvement in data architecture, engineering, governance, quality, observability, and operational excellence. Data Architecture & Technical Leadership Lead the architecture and design of complex, enterprise-scale data solutions across cloud, lakehouse, warehouse, streaming, and analytical platforms. Define scalable architectural patterns for data ingestion, transformation, storage, integration, consumption, and lifecycle management. Provide technical direction for complex initiatives involving multiple applications, data domains, platforms, and engineering teams. Establish standards for data modeling, interoperability, metadata, lineage, security, performance, and platform integration. Lead technical design reviews and evaluate architectural trade-offs involving scalability, reliability, maintainability, performance, security, and cost. Serve as a technical escalation point for complex data architecture, engineering, integration, and production challenges. Guide modernization of legacy data platforms and workloads using modern cloud and distributed data technologies. Advanced Data Engineering Design and lead implementation of highly scalable batch, streaming, and event-driven data processing solutions. Develop complex data solutions using Python, PySpark, Scala, SQL, or comparable engineering languages. Apply distributed processing technologies such as Apache Spark, Kafka, Flink, dbt, or equivalent modern data frameworks. Design reusable engineering frameworks and patterns that accelerate delivery across multiple data products and teams. Design dimensional, relational, lakehouse, and domain-oriented data models appropriate for operational and analytical use cases. Optimize distributed workloads, SQL queries, storage organization, partitioning strategies, and data pipelines for performance, scalability, and cost. Establish advanced practices for automated testing, dependency management, orchestration, failure recovery, replay, and operational resilience. Microsoft Fabric & Cloud Data Platforms Provide technical leadership for modern data solutions using Microsoft Fabric, including Lakehouse, Warehouse, OneLake, Data Pipelines, Notebooks, Dataflows Gen2, and Semantic Models. Design scalable processing solutions using Fabric Notebooks, Spark, PySpark, Spark SQL, and Delta Lake. Define patterns for data organization, ingestion, transformation, orchestration, and consumption within Microsoft Fabric. Apply appropriate Azure and/or AWS cloud services to enterprise data architectures. Evaluate emerging platform capabilities and determine where they can improve engineering productivity, scalability, reliability, and cost efficiency. Engineering Excellence & DevOps Define and promote software engineering standards for data development, including Git-based version control, code review, automated testing, CI/CD, and automated deployment. Establish reusable development patterns, coding standards, testing strategies, and deployment practices. Design observability strategies covering pipeline execution, data quality, dependencies, application health, performance, and failures. Drive proactive monitoring and operational excellence using platforms such as Datadog or equivalent observability technologies. Lead root-cause analysis for complex production issues and drive solutions that prevent recurrence. Establish measurable engineering practices that improve reliability, maintainability, performance, and supportability. Data Quality, Governance & Security Define and implement enterprise-level practices for data quality, metadata management, lineage, retention, classification, and lifecycle management. Establish automated data validation, profiling, reconciliation, and quality monitoring practices. Partner with business owners, data custodians, governance teams, security teams, and architects to establish trusted and governed data products. Ensure solutions align with applicable privacy, security, regulatory, retention, and enterprise data governance requirements. Establish clear ownership and accountability for critical data elements and governed data products. Design and communicate standards for documenting data sources, transformations, lineage, business rules, and downstream dependencies. Evaluate new data technologies, architectural patterns, and frameworks and recommend adoption where they provide measurable value. Provide technical leadership for modernization initiatives and migration from legacy data ecosystems to modern cloud and lakehouse platforms. Apply generative AI and agentic AI tools and patterns where appropriate to improve data engineering productivity, metadata management, data quality, and operational processes. Develop prototypes and reference implementations to validate emerging technologies and architecture patterns. Partner with senior business and technology leaders to translate complex business needs into long-term data capabilities. Communicate complex technical topics, architectural decisions, risks, and trade-offs to both technical and non-technical audiences. Mentor engineers and help develop advanced engineering and architecture capabilities across the organization. Create technical documentation, reference architectures, standards, and reusable implementation guidance. Expert-level knowledge of data architecture, data engineering, distributed processing, data integration, and data modeling. Advanced proficiency in Python, PySpark, Scala, SQL, or comparable languages used for large-scale data processing. Advanced understanding of Microsoft Fabric, including Lakehouse, Warehouse, OneLake, Notebooks, Data Pipelines, Dataflows Gen2, and Semantic Models. Advanced SQL expertise, including query optimization and performance tuning. Strong understanding of relational, dimensional, NoSQL, and distributed data modeling techniques. Experience designing reusable frameworks and enterprise-scale data processing patterns. Strong knowledge of cloud architecture and data services within Microsoft Azure and/or AWS. Advanced understanding of engineering practices including Git, CI/CD, automated testing, infrastructure/deployment automation, and release management. Experience with observability platforms such as Datadog and designing operational monitoring for enterprise data solutions. Strong understanding of data governance, metadata, lineage, data quality, security, privacy, and retention principles. Demonstrated ability to troubleshoot complex distributed data and production problems. Experience designing large-scale solutions on Microsoft Fabric and Azure. Experience modernizing legacy data warehouse, ETL, or enterprise data processing environments into cloud/lakehouse architectures. Experience developing metadata-driven data processing and orchestration frameworks. Experience with event-driven and real-time data architectures. Experience establishing enterprise data engineering standards, reusable frameworks, or reference architectures. Experience with data cataloging, lineage, governance, and observability capabilities. Experience applying generative AI and agentic AI technologies to data engineering and operational use cases. Experience leading architecture reviews, proof-of-concepts, technology evaluations, and complex platform modernization efforts. 10 or more years of relevant technology, data engineering, data architecture, or data platform experience (Preferred). ~ Significant experience designing and delivering complex, enterprise-scale data solutions. ~ Data Analysis, Data Governance, Data Modeling, Data Pipelines, Enterprise Data, Technical Strategy

Compensation Allstate generally does not sponsor individuals for employment-based visas for this position. Effective July 1, 2014, under Indiana House Enrolled Act (HEA) 1242, it is against public policy of the State of Indiana and a discriminatory practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by refusing to employ an applicant on the basis that they are a veteran of the armed forces of the United States, a member of the Indiana National Guard or a member of a reserve component. This poster provides information concerning the laws and procedures for filing complaints of violations of the laws with the Office of Federal Contract Compliance Programs. This poster summarizing the major provisions of the Family and Medical Leave Act (FMLA) and telling employees how to file a complaint. It is the Company’s policy to employ the best qualified individuals available for all jobs. Therefore, any discriminatory action taken on account of an employee’s ancestry, age, color, disability, genetic information, gender, gender identity, gender expression, sexual and reproductive health decision, marital status, medical condition, military or veteran status, national origin, race (include traits historically associated with race, including, but not limited to, hair texture and protective hairstyles), religion (including religious dress), sex, or sexual orientation that adversely affects an employee's terms or conditions of employment is prohibited. This policy applies to all aspects of the employment relationship, including, but not limited to, hiring, training, salary administration, promotion, job assignment, benefits, discipline, and separation of employment. Employees eligible to work from home also receive a monthly connectivity reimbursement to help offset internet costs. When working from home, you must have a dedicated, private workspace free from distractions, along with appropriate desk and seating. To help create a more personal and engaging experience, we ask that you join with your camera on if your interview is virtual. Note: Internal candidates are only asked to join on video;