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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineering Manager, Data Engineering - **Company:** MAPFRE - **Location:** Webster, MA, United States - **Experience:** Experienced - **Salary:** $100,000.0 - $152,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Business Analytics Applications, Apache HTTP Server, Application Frameworks, Automation of Tests, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Dataspaces, Data Systems, Data Warehousing, Github, Python (Programming Language), Meta-Data Management, Cloud Services, DataOps, Azure Machine Learning, Software Engineering, SQL Databases, Data Streaming, Enterprise Data Management, Snowflake, Technical Debt, Infrastructure Automation Frameworks, Information Technology, Data Lineage, Deployment Automation, Apache Kafka, Data Management, Machine Learning Operations, Data Pipelines, Guidewire - **Published:** July 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=19db5c670a73f936 ## About the Role * Bachelor's Degree in Computer Science, Engineering, Information Systems, or related field. Master's Degree preferred. * 8+ years of Data Engineering, Software Engineering, or Platform Engineering experience. * 3+ years leading engineering teams. * Experience building and supporting enterprise-scale cloud data platforms. * Experience managing Agile delivery teams. * Experience delivering data products and analytics solutions. Technical Expertise * Strong experience with: + AWS + Snowflake + SQL + Python + Data Warehousing + Lakehouse Architectures + Data Modeling + CI/CD + GitHub Actions + ETL / ELT Engineering Data Observability * Preferred experience: + DBT + Apache Iceberg + Airflow + Starburst + Kafka + Guidewire ecosystem + AI/ML Platforms + MLOps Concepts ## Description Mapfre is seeking a highly motivated and experienced Engineering Manager, Data Engineering to lead the design, development, and operation of enterprise-scale data platforms and data products that power analytics, AI, reporting, regulatory compliance, and business decision-making. This leader will manage a team of Data Engineers and Technical Leads responsible for delivering high-quality, scalable, secure, and reliable data solutions. The Engineering Manager will partner closely with Data Product Managers, Architects, Business Stakeholders, and Corporate Data Platform teams to deliver business value while advancing MAPFRE's Data maturity and transformation strategy. The successful candidate will drive engineering excellence, operational reliability, talent development, and innovation while supporting strategic initiatives. Responsibilities: Engineering Leadership * Lead and mentor a team of Data Engineers and Platform Engineers. * Establish a culture of engineering excellence, ownership, collaboration, innovation, and continuous improvement. * Develop team capabilities through coaching, technical mentorship, career development, and succession planning. * Foster a high-performance, psychologically safe, and inclusive engineering culture. Delivery & Execution * Own the successful delivery of enterprise data solutions and data products. * Manage engineering capacity, sprint commitments, dependencies, risks, and delivery timelines. * Partner with Data Product Managers to translate business requirements into scalable technical solutions. * Ensure predictable and high-quality delivery. * Drive reduction of technical debt while balancing business priorities. Data Platform & Architecture * Lead engineering efforts across the MAPFRE data ecosystem including: + Data Platform + Snowflake + AWS Services + Streaming and CDC Pipelines + Data Warehousing & Lakehouse Architectures + Data Observability * Collaborate with Architecture Team, Application teams and the Data Architecture teams to ensure alignment with enterprise standards. * Promote reusable frameworks, automation, and engineering best practices. * Retire legacy platform Operational Excellence * Advance DataOps and FinOps maturity across engineering teams. * Implement CI/CD, Infrastructure as Code, automated testing, monitoring, observability, and operational controls. * Improve reliability, scalability, recoverability, and performance of data products and pipelines. * Define and manage engineering KPIs and service-level objectives. * Key focus areas include: + Automated deployments + Data observability + Pipeline monitoring + Cost optimization (FinOps) + Self-healing data pipelines + Operational dashboards Data Quality & Governance * Partner with Data Governance, Data Product Management, and Business stakeholders to improve trust in enterprise data. * Ensure engineering solutions support: + Data lineage + Data quality controls + Regulatory compliance + Metadata management + Data product registration and discoverability * Support Mapfre's "Governance by Design" principles. Stakeholder Management * Build strong relationships with business partners, product teams, analytics teams, and corporate platform teams. * Communicate engineering priorities, progress, risks, and accomplishments to technical and executive audiences. * Serve as a trusted advisor on technology strategy and solution delivery. * Balance strategic objectives with operational commitments. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)