Data & Analytics Expert

Allstate • McCullom Lake, IL

6d • $120,000 - $193,725

About The Position

The Data Expert provides technical leadership in designing, engineering, governing, and evolving scalable, reliable, secure, and well-governed enterprise data solutions. This senior technical authority translates business and analytical needs into trusted, reusable data products and leads complex initiatives across multiple systems, data domains, platforms, and engineering teams.

The role defines modern data architectures, engineering standards, and best practices; guides modernization of legacy platforms; and solves complex data challenges. It works with product managers, architects, engineers, analytics teams, business stakeholders, governance teams, and security teams to develop enterprise data capabilities. The position operates with a high degree of autonomy, influences technical direction without direct supervisory authority, mentors engineers, and drives continuous improvement in architecture, engineering, governance, quality, observability, and operational excellence.

The role includes leading enterprise-scale solutions across cloud, lakehouse, warehouse, streaming, and analytical platforms; advanced batch, streaming, and event-driven data engineering; and technical leadership for Microsoft Fabric and Azure and/or AWS services. It also establishes engineering practices for version control, code review, testing, CI/CD, deployment, monitoring, resilience, data quality, metadata, lineage, security, privacy, retention, and governance. The expert evaluates emerging technologies, develops prototypes and reference implementations, and applies generative AI and agentic AI where appropriate to improve data engineering and operational processes.

Compensation is $120,000–$193,725 annually, based on experience and qualifications. Candidates offered the position must submit to a background investigation. Allstate generally does not sponsor individuals for employment-based visas for this position.

Requirements

• 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.
• Strong knowledge of Delta Lake, lakehouse architecture, distributed computing, batch processing, and streaming architectures.
• 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 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.
• Demonstrated ability to lead technical initiatives and influence engineering standards across multiple teams.
• Excellent communication skills, including the ability to communicate architecture and technical strategy to engineering teams, business partners, and senior leadership.
• Significant experience designing and delivering complex, enterprise-scale data solutions.
• Demonstrated experience providing technical leadership across large or cross-functional initiatives.

Nice To Haves

• 10 or more years of relevant technology, data engineering, data architecture, or data platform experience (preferred).
• 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.

Responsibilities

• Lead the architecture and design of complex, enterprise-scale data solutions across cloud, lakehouse, warehouse, streaming, and analytical platforms.
• Define scalable architecture patterns for data ingestion, transformation, storage, integration, consumption, and lifecycle management.
• Provide technical direction for initiatives spanning 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 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.
• Design and lead scalable batch, streaming, and event-driven data processing solutions.
• Develop complex data solutions using Python, PySpark, Scala, SQL, or comparable engineering languages and modern distributed processing frameworks.
• Design reusable engineering frameworks, patterns, and data models for operational and analytical use cases.
• Optimize distributed workloads, SQL queries, storage organization, partitioning strategies, and pipelines for performance, scalability, and cost.
• Establish practices for automated testing, dependency management, orchestration, failure recovery, replay, and operational resilience.
• Provide technical leadership for Microsoft Fabric solutions, including Lakehouse, Warehouse, OneLake, Data Pipelines, Notebooks, Dataflows Gen2, and Semantic Models.
• Define data organization, ingestion, transformation, orchestration, and consumption patterns within Microsoft Fabric.
• Apply appropriate Azure and/or AWS services to enterprise data architectures and evaluate emerging platform capabilities.
• Define 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 for pipeline execution, data quality, dependencies, application health, performance, and failures; drive proactive monitoring and operational excellence.
• Lead root-cause analysis of complex production issues and implement solutions to prevent recurrence.
• Define and implement enterprise 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, governed data products aligned with privacy, security, regulatory, retention, and enterprise governance requirements.
• Establish ownership and accountability for critical data elements and governed data products, and document sources, transformations, lineage, business rules, and dependencies.
• Evaluate data technologies, architecture patterns, and frameworks; recommend adoption where they offer measurable value.
• Identify opportunities to simplify architecture, reduce technical debt, improve development velocity, and increase reuse.
• Apply generative AI and agentic AI tools and patterns where appropriate to improve engineering productivity, metadata management, data quality, and operations.
• Develop prototypes and reference implementations to validate emerging technologies and architecture patterns.
• Partner with senior business and technology leaders to translate business needs into long-term data capabilities.
• Communicate technical topics, architectural decisions, risks, and trade-offs to technical and non-technical audiences.
• Facilitate discussions across engineering, product, analytics, governance, and business teams, and influence technical direction across teams without direct supervisory authority.
• Mentor engineers and create technical documentation, reference architectures, standards, and reusable implementation guidance.

Benefits

• Comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse.
• Monthly connectivity reimbursement for employees eligible to work from home.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed