Data-AI Architect

Join dLocal as a Data-AI Architect, where you will shape a scalable and governed data ecosystem. You will provide technical direction for data and analytics architecture, define enterprise data architectures, lead the adoption of data-mesh principles, and establish reference architectures and engineering standards. You will also guide the evolution of cloud data platforms, provide architectural direction for MLOps, and act as a trusted advisor and technical mentor to various teams.

Demonstrated ability to design for real-time and near-real-time workloads, including latency measurement, event-time semantics, late data, state, deduplication, idempotency, replay, and failure recovery

About dLocal

Strong understanding of reliability, scalability, performance, resilience, cost, and vendor lock-in trade-offs

Responsibilities

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

Requirements

• 8–10+ years of experience designing and operating scalable data architectures, preferably in complex enterprise or high-growth environments
• Experience with cloud data platforms and services, particularly AWS and/or GCP; experience with Databricks, Unity Catalog, Delta Lake, Iceberg, or comparable technologies is valuable
• Strong knowledge of semantic layers, business ontologies, canonical data models, knowledge graphs or metadata models, metric definitions, and semantic governance
• Comfortable managing risk, ambiguity, and conflict; able to make decisions and explain the reasoning behind them
• Excellent stakeholder-management, communication, facilitation, and influencing skills, including the ability to balance delivery expectations and technical excellence
• Self-sufficient and proactive, with the judgment to know when to seek input and when to move forward
• Ability to establish practical frameworks for data access, stewardship, governance, privacy, security, quality, and operational accountability
• Proficiency with relevant data and platform technologies such as Spark, Airflow, dbt, Kafka, Python, SQL, CI/CD, infrastructure-as-code, and observability tooling
• Expertise in data modeling, data lake and lakehouse patterns, warehouse design, data pipelines, data products, metadata, lineage, and data management technologies
• 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
• Experience architecting or supporting MLOps, feature stores, online/offline data serving, or other low-latency machine-learning data systems