Data-AI Architect
DLOCAL · São Paulo, SP, BR
**Why Join dLocal?** dLocal is the financial infrastructure powering global commerce in the world's fastest\-growing markets. The biggest companies in the world trust us to unlock growth in 60\+ countries across emerging markets—moving money where others see complexity. We don't just process payments; we are architects of payment ecosystems and partners in our customers' expansion. You'll work alongside 1,300\+ teammates from 40\+ nationalities and tackle global challenges from day one. ### **What’s the opportunity?** We are looking for a Data Architect to provide the technical direction for dLocal’s data and analytics architecture. This role will shape a scalable, governed, and highly consumable data ecosystem across Data \& AI and engineering—connecting domain\-owned data products, real\-time platforms, analytical workloads, machine\-learning use cases, and business\-facing data consumption. You will act as a senior technical reference for architecture decisions, translating business and product needs into pragmatic designs that balance scalability, reliability, latency, security, interoperability, developer experience, and total cost of ownership. ### **What will I be doing?** * Define and evolve enterprise data architectures, evaluate trade\-offs, and recommend fit\-for\-purpose technology patterns across batch, streaming, lakehouse, warehouse, and operational use cases. * Review and advise other architects on the data aspects of their RFCs, helping ensure consistency with enterprise data principles, governance standards, and architectural direction. * Lead the adoption of data\-mesh principles, including domain\-oriented ownership, data as a product, federated computational governance, self\-serve platform capabilities, discoverability, quality, and measurable data\-product SLAs. * Establish reference architectures and engineering standards for data products, pipelines, ingestion, storage, processing, orchestration, observability, lineage, security, and access management. * Provide oversight on operational SLAs, including latency, cost, quality, freshness, reliability, and production performance. * Design and govern streaming architectures using technologies such as Kafka, Kinesis, Flink, Spark Structured Streaming, and Databricks, supporting use cases from scheduled batch through sub\-second real\-time processing. * Define reliable event\-processing patterns, including schema and data contracts, schema registries, event\-time processing, late\-event handling, idempotency, deduplication, replay and reprocessing, dead\-letter flows, and freshness SLAs. * Shape semantic layers and enterprise ontologies that create consistent business meaning across domains, including canonical entities, metrics, dimensions, relationships, business definitions, metadata, lineage, and versioning. * Establish patterns that allow semantic models to serve analytics, operational applications, machine learning, and AI use cases without creating duplicated or contradictory definitions. * Guide the evolution of cloud data platforms and lakehouse capabilities, including Databricks, Unity Catalog, Delta/Iceberg tables, object storage, data warehouses, and BI consumption layers across AWS and GCP environments. * Provide architectural direction for MLOps and feature\-platform capabilities, including batch and online features, model\-serving integrations, low\-latency data paths, model/data lineage, monitoring, and governance. * Lead or contribute to architecture RFCs, technical decisions, design reviews, migration plans, and implementation roadmaps; make complex trade\-offs clear to both technical and non\-technical stakeholders. * Partner with domain teams to clarify ownership, data\-product responsibilities, operational handover, quality accountability, access approval, and cross\-domain consumption models. * Define practical controls for data quality, observability, privacy, security, resilience, cost management, and production readiness. * Take ownership of critical architectural issues, facilitate resolution across teams, and ensure decisions are followed through to implementation and operation. * Act as a trusted advisor and technical mentor to data engineers, platform teams, data scientists, MLOps engineers, BI teams, and engineering leaders. * Communicate a cohesive architectural vision while remaining pragmatic, adaptable, and close enough to implementation to validate that designs work in production. ### **What skills do I need?** * 8–10\+ years of experience designing and operating scalable data architectures, preferably in complex enterprise or high\-growth environments. * Strong experience designing and implementing data\-mesh architectures and operating models, including domain ownership, data products, federated governance, self\-serve platforms, contracts, quality, and discoverability. * Deep experience with streaming and event\-driven architectures, including Kafka or Kinesis and one or more processing engines such a