{"id":2117723,"url":"https://alion.io/job/mdotm-data-engineer-latam","title":"Data Engineer LATAM","company":{"id":3882794,"name":"Mdotm","domain":"mdotm.ai","url":"https://alion.io/company/mdotm-2","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","truth_index":{"grade":"C","score":60,"open_postings":18,"ghost_share":0.667,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-06-16T00:00:00Z","employer_posted_date":"2026-06-16","last_verified_at":"2026-10-13T20:07:13Z","board_verified":true,"closed_at":null,"days_open":119,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":119},"description":"About MDOTM\nMDOTM is a global leading provider of AI-driven investment solutions, growing across our offices in London, Milan, and New York. We recently closed a $27M growth equity round, led by Expedition Growth Capital, to support our continued international expansion. Our proprietary platform, Sphere, currently supports over $100B in assets under management for institutional clients including Morgan Stanley, Amundi, and Zurich Bank.\nRole Overview\nWe are looking for a Data Engineerto play a key role in shaping and scaling our platform as the organization grows in complexity and data maturity.\nIn this role, you will be responsible for designing and evolvingthe core data infrastructure that powers analytics, machine learning, and critical business decisions. You will take ownershipof how data flows across the company, from ingestion to consumption, ensuring it is reliable, well-modeled, and accessible to a wide range of stakeholders.\nKey Responsibilities\nDesign, build, and maintain production-grade data pipelinesand data-intensive systems\nConfigure, schedule, and monitor data pipeline execution to ensure reliability, maintainability, and timelydelivery across all data processes\nDeploy and manage data infrastructure on AWS or on-premises, ensuring scalability, security, and cost-efficiency\nMonitor and optimize relational and NoSQL databaseperformance (e.g., MySQL, PostgreSQL, MongoDB) to ensure efficient querying, indexing, and data access at scale\nEnsure reliable ingestion, transformation, and availability of large-scale and time-series financial data, considering financial-specific data quality characteristics.\nImplement and maintain data quality, validation, and monitoringmechanisms across data workflows\nDefine and evolve data modelsand storage structures across relational and NoSQL systems\nCollaborate with Data Scientists and Analyststo ensure accurate and efficient data access for analytics and modeling use cases\nOptimize data processing workflowsfor performance, reliability, and operational stability\nTroubleshootcomplex data issues and drive root cause analysis.\nContribute to technical decision-makingand help define the roadmap for data infrastructure.\nRequirements\nDegree in Computer Science, Engineering, or a related field.\nStrong programming skills in Java or Python (or other JVM-based languages)\nStrong understanding of data modeling, SQL, and NoSQL databases\nStrong focus on data quality, validation, and monitoring practices\nExperience working with large-scale and time-series datasets in financial contexts, with an understanding of their structure and common data quality challenges\nExperience building and maintaining production data pipelines or data-intensive systems, with focus on reliability and performance\nAbility to troubleshoot and optimize production data systems\nExperience with workflow orchestration and data pipeline scheduling tools\nSolid understanding of financial data structures and concepts, with the ability to model and validate financial data accurately\nStrong problem-solving skills and attention to detail.\nProfessional proficiency in English.\nBonus Points\nExperience with monitoring and visualization tools (e.g., dashboards)\nExposure to machine learning workflows and related data requirements\nWhy Join Us?\nWork at the leading edge of technology, leveraging our decade of experience in proprietary AI to build the next generation of industry-defining tools.\nCompetitive salary & trulyflexible work environment.\nBenefit from an unlimited learning and development budget to stay at the bleeding edge of AI research, alongside a fast-track path into technical leadership or principal research roles.\nCollaborate daily with anultra-international 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