Data-AI Architect

Job description

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?
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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.

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Review and advise other architects on the data aspects of their RFCs, helping ensure consistency with enterprise data principles, governance standards, and architectural direction.

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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.

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Establish reference architectures and engineering standards for data products, pipelines, ingestion, storage, processing, orchestration, observability, lineage, security, and access management.

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Provide oversight on operational SLAs, including latency, cost, quality, freshness, reliability, and production performance.

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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.

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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.

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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.

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Establish patterns that allow semantic models to serve analytics, operational applications, machine learning, and AI use cases without creating duplicated or contradictory definitions.

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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.

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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.

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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.

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Partner with domain teams to clarify ownership, data-product responsibilities, operational handover, quality accountability, access approval, and cross-domain consumption models.

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Define practical controls for data quality, observability, privacy, security, resilience, cost management, and production readiness.

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Take ownership of critical architectural issues, facilitate resolution across teams, and ensure decisions are followed through to implementation and operation.

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Act as a trusted advisor and technical mentor to data engineers, platform teams, data scientists, MLOps engineers, BI teams, and engineering leaders.

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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?
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8–10+ years of experience designing and operating scalable data architectures, preferably in complex enterprise or high-growth environments.

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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.

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Deep experience with streaming and event-driven architectures, including Kafka or Kinesis and one or more processing engines such as Flink or Spark S

Prepare for the interview

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