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
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.
Key Responsibilities:
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,…