Managing Engineer, Data Engineering

Allstate is an insurance company focused on protecting families and their belongings from life’s uncertainties through innovation. The Managing Engineer, Data Engineering will lead the design, development, and delivery of scalable data solutions while providing hands-on engineering, technical leadership, team development, and delivery accountability. The role partners with product, engineering, architecture, analytics, security, governance, and business stakeholders to deliver reliable, secure, and high-performing data products.

Responsibilities:

• Lead, mentor, and coach Data Engineers while supporting their technical and professional development
• Promote a culture of collaboration, accountability, knowledge sharing, and continuous improvement
• Establish engineering standards and encourage consistent use of best practices
• Balance hands-on engineering with technical leadership, team development, and delivery responsibilities
• Depending on the position level, support or lead hiring, onboarding, performance feedback, career development, and workforce planning
• Lead the design, development, implementation, and support of scalable batch and streaming data solutions
• Develop reusable patterns for data ingestion, transformation, storage, and access
• Integrate data from APIs, databases, event streams, third-party platforms, and enterprise systems
• Remain actively involved in architecture discussions, solution design, code reviews, troubleshooting, and complex problem-solving
• Ensure data solutions are reliable, maintainable, secure, scalable, and cost-effective
• Promote modern engineering practices, including version control, automated testing, continuous integration, automated deployment, and infrastructure automation
• Establish standards for data quality, validation, monitoring, lineage, and observability
• Optimize data pipelines, databases, queries, and storage solutions for performance and cost
• Support production operations and provide leadership during critical incidents, root-cause analysis, and preventative improvements
• Identify opportunities to reduce technical debt and operational risk
• Partner with Product Managers, Architects, Engineers, Analysts, and business stakeholders to understand priorities and deliver effective data solutions
• Translate business and product needs into technical roadmaps and clearly defined engineering work
• Lead or contribute to planning, prioritization, estimation, and delivery activities
• Manage risks, dependencies, resource needs, and competing priorities
• Communicate progress, technical recommendations, risks, and dependencies to stakeholders and leaders
• Promote data governance practices, including metadata management, data lineage, classification, and data quality
• Ensure solutions meet applicable security, privacy, governance, and regulatory requirements
• Partner with Architecture, Security, Risk, and Governance teams to align solutions with enterprise standards
• Support the responsible access, use, storage, and movement of enterprise data
• Evaluate emerging technologies, frameworks, and engineering practices that may improve data capabilities
• Identify opportunities to increase automation, simplify development, and improve engineering productivity
• Support cloud modernization and data platform transformation initiatives
• Promote continuous learning and the responsible use of generative and agent-based artificial intelligence tools

Requirements:

• 5 or more years of experience designing, developing, and supporting data solutions, data pipelines, or enterprise software systems
• 1 or more years of experience providing technical leadership, mentorship, coaching, or project leadership
• Experience guiding engineers through technical mentorship and knowledge sharing
• Strong hands-on experience developing data pipelines using Python, Java, Scala, or a comparable programming language
• Experience designing and supporting batch or streaming data solutions
• Experience with Apache Spark, Apache Kafka, Apache Flink, dbt, or comparable technologies
• Strong knowledge of SQL, NoSQL databases, data modeling, and data storage concepts
• Experience integrating data from APIs, databases, event streams, and third-party systems
• Experience with automated testing, continuous integration and delivery, deployment automation, and DevOps practices
• Experience with Docker and Kubernetes
• Strong communication, collaboration, and stakeholder management skills
• Ability to influence technical outcomes and lead through expertise
• 7 or more years of experience designing, developing, and supporting data solutions, data pipelines, or enterprise software systems
• 3 or more years of experience leading, mentoring, or managing technical professionals
• Experience with coaching, performance management, career development, hiring, and team development
• Experience designing and supporting scalable batch and streaming data solutions
• Strong knowledge of SQL, NoSQL databases, data modeling, and data architecture
• Experience integrating data from APIs, databases, event streams, and enterprise systems
• Experience optimizing data platforms, pipelines, databases, and storage solutions for performance and cost
• Experience implementing automated testing, continuous integration and delivery, observability, and engineering excellence practices
• Strong communication, decision-making, and stakeholder management skills
• Ability to balance hands-on engineering, people leadership, and delivery accountability
• Allstate generally does not sponsor individuals for employment-based visas for this position
• Experience with data services and platforms in Microsoft Azure or Amazon Web Services
• Hands-on experience with Microsoft Fabric, including Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2, Semantic Models, and Fabric Notebooks
• Experience with Azure DevOps, GitHub Actions, Jenkins, or similar tools
• Experience with Datadog or another monitoring and observability platform
• Experience supporting or leading cloud modernization or data platform transformation initiatives
• Experience building reusable data frameworks, shared services, internal platforms, or developer tools
• Knowledge of data governance, metadata management, data lineage, privacy, security, and regulatory requirements
• Experience supporting large-scale enterprise data products
• Familiarity with generative and agent-based artificial intelligence tools used to improve engineering productivity