- 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

- 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

📌 Data-AI Architect (Buenos Aires)
🏢 Dlocal
📍 Buenos Aires