Managing Engineer, Data Engineering

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 We are seeking Two Managing Engineers, Data Engineering to lead the design, development, and delivery of scalable data solutions that support digital products, analytics, and business decision-making. Both opportunities combine hands-on engineering with technical leadership, team development, and delivery responsibility. You will partner with Product Managers, Software Engineers, Data Analysts, Architects, and business stakeholders to deliver reliable, secure, and high-performing data products. Based on an assessment of each candidate's experience, technical expertise, and leadership background, candidates may be considered for the opportunity that best aligns with their qualifications. Key Responsibilities People and Engineering Leadership 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. Data Solution Design and Development 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. Engineering Excellence and Production Support 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. Product Collaboration and Delivery 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. Data Governance, Security, and Standards 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. Innovation and Continuous Improvement 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. Essential Qualifications Managing Engineer: 5 or More Years of Experience 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. Managing Engineer: 7 or More Years of Experience 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. Strong hands-on experience developing data pipelines using Python, Java, Scala, or a comparable programming language. Experience designing and supporting scalable batch and 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 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. Experience with Docker and Kubernetes. Strong communication, decision-making, and stakeholder management skills. Ability to balance hands-on engineering, people leadership, and delivery accountability. Desirable Qualifications 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. Supervisory Responsibilities This job has supervisory duties #LI-MF1 Skills Amazon Web Services (AWS), Application Programming Interface (API), Data Architecture Development, Data Engineering, Data Ingestion, Data Pipelines, Data Stream, Enterprise Data, Kubernetes, Microsoft Azure, People Management, Python (Programming Language) Compensation Compensation offered for this role is 120,000.00 - 193,7