Role Description Consult on designing and developing programs and tools to support ingestion, curation, and provisioning of highly complex enterprise data to achieve analytics, reporting, and data science. - Involved in and contribute to development and enhancement of solutions for gaps. - Responsible for designing and building highly complex data models and data architecture that improve accessibility, efficiency, governance, and data quality that directly impact business results. - Understand and develop business process improvements that address the most complex technology gaps within a single business process. - Develop technology enabled recommendations to address gaps within a single business process. - Document existing processes, organizational structures, architectures, and external systems. - Consult on the appropriate tools to prepare deliverables. - Participate in the development of tools and methodologies and adapt them to the organization environment. - Manage peer to peer relationships, with colleagues, vendors and external contractors, including offshore functions. - Assist with development and execution of cutover plans for simple processes. - Assist in post-implementation support, resolving issues and raising defects to appropriate level. - Execute transition plan for area of responsibility to the appropriate resource for each phase of the project or initiative. - Transition the knowledge sharing and transfers ownership to other resources or teams for key deliverables and business processes. - Domestic travel required up to 10%. - Telecommuting permitted up to 100%. Qualifications - Employer will accept a Bachelor’s degree in Computer Science, Engineering [any] or related field and five (5) years of experience in the job offered or in a Solutions Engineer, Data-related occupation. - Position requires demonstrable experience in the following: - Design and implement data solutions of high complexity including data solutions for applications with a Mean Time To Recovery of 4 hours or less. - Work closely with application team to ensure data needs meet business needs and align with architecture standards for support and security. - Create applications with a high degree of observability utilizing Observability tools including Datadog. - Guide senior level engineers on design practices and work closely with architects to influence strategic decisions. - Drive the identification of key enablers and maintain a 12-month roadmap for aligned software products to meet business needs. - Proficiency with Relational Databases and NoSQL databases including Oracle. - Proficiency with cloud-based providers and containerized solutions including AWS. - Experience may be gained during graduate program. - Will accept any suitable combination of education, training, and/or experience. - Multiple Positions Available. Requirements - To apply, please visit https://jobs.libertymutualgroup.com/ , select “Search Jobs,” enter job requisition #2026-76935 in the “Job ID or Keywords” field, and submit resume. - Alternatively, you may apply by submitting a resume via e-mail to RecruitLM@LibertyMutual.com . Reference requisition number in subject of e-mail. Benefits - The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. - The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. - Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role. - We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well-being. - To learn more about our benefit offerings please visit: https://www.libertymutualgroup.com/about-lm/careers/benefits Company Description Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran's status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.

• Design, develop, and maintain scalable and efficient data pipelines to process data from various sources using tools such as Matillion, HVR and Databricks • Collaborate with data scientists, analysts, architects and other stakeholders to understand and document data requirements and translate them into technical solutions • Develop and optimize ETL (Extract, Transform, Load) processes to ensure data quality and integrity • Utilize Snowflake for data warehousing, ensuring efficient data storage and retrieval • Perform data modeling to design and structure databases that support business requirements • Write and optimize SQL queries for data transformation • Ensure data security and compliance with industry standards and regulations • Adhere to and advocate established best practices, design patterns, and data governance mandates • Document data processes, workflows, and technical specifications • Conduct data analysis to support business insights and decision-making

• Own the end-to-end design, delivery, and operation of major data products, including high-throughput ETL/ELT pipelines, transformation workflows, and storage solutions. • Partner with engineering, product, and business stakeholders to clarify requirements, challenge assumptions, define pragmatic solutions, and drive projects from design through production launch. • Solve difficult data engineering problems by developing performance-tuned transformations and services using Python, high-performance SQL, and tools such as dbt. • Make sound technical trade-offs at the product level, balancing scalability, reliability, delivery speed, maintainability, and cost. • Author and drive technical designs and RFCs that clearly communicate architecture, alternatives, risks, and operational considerations. • Influence the technical direction of the data products you own and contribute to the team’s broader technical strategy and roadmap. • Set a high quality bar through thoughtful design and code reviews, automated testing, version-controlled schemas, and CI/CD practices for data pipelines. • Lead projects that improve the team’s engineering and operational excellence, including data observability, lineage, incident response, deployment safety, and developer productivity. • Design and operate scalable batch and real-time data solutions using technologies such as Spark, Flink, and Kafka. • Partner with the ML Platform team to deliver clean, reliable, and feature-rich datasets for model training and inference. • Advance data stewardship through strong documentation, discoverability, privacy controls, access management, and governance practices. • Lead postmortems for data incidents, identify systemic improvements, and drive corrective actions to completion. • Break complex projects into clear, deliverable work and help coordinate execution across engineers on the team. • Mentor other engineers, provide actionable technical feedback, and participate in interviewing and hiring. • Participate in the team's on-call rotation, responding to incidents, troubleshooting issues, and contributing to root cause analysis and service reliability improvements. • Respond to and troubleshoot production issues, outages, and critical alerts during assigned on-call shifts. • Escalate incidents as needed and collaborate with team members to restore service. • Document issues and contribute to improvements that reduce recurring incidents. • Be available to provide occasional after-hours, weekend, and holiday support while on call.

• Design, build, and operate large-scale data processing pipelines handling multi-terabyte and streaming datasets, including audio/video transcoding, feature extraction, and preprocessing workflows. • Deploy, scale, and troubleshoot containerized workloads on Kubernetes and AWS in production environments. • Build and maintain distributed data processing jobs using frameworks such as Spark and Ray. • Design and operate workflow orchestration systems (e.g., Airflow) with dependency management, retries, monitoring, and alerting for production pipelines. • Administer and tune enterprise databases, including performance tuning, backup/recovery, access control, and scaling strategies. • Partner with ML engineers and researchers to support training pipelines, model retraining triggers, feature stores, and other MLOps workflows.