SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
dLocal is the financial infrastructure powering global commerce in emerging markets, trusted by the world's largest companies to unlock growth across 60+ countries. The company moves money where others see complexity, operating as architects of payment ecosystems rather than transaction processors.
As a Data-AI Architect, you will define and evolve enterprise data architectures across batch, streaming, lakehouse, warehouse, and operational use cases. You'll evaluate technology trade-offs, recommend fit-for-purpose patterns, and review other architects' data aspects of RFCs to ensure consistency with enterprise principles and governance standards.
Key responsibilities include leading the adoption of data-mesh principles—domain-oriented ownership, data as a product, federated computational governance, and self-serve platform capabilities. You'll establish reference architectures and engineering standards for data products, pipelines, ingestion, storage, processing, orchestration, observability, lineage, security, and access management. You'll design and govern streaming architectures using Kafka, Kinesis, Flink, Spark Structured Streaming, and Databricks, supporting use cases from scheduled batch through sub-second real-time processing.
You'll shape semantic layers and enterprise ontologies that create consistent business meaning across domains, including canonical entities, metrics, dimensions, and relationships. You'll guide the evolution of cloud data platforms and lakehouse capabilities on AWS and GCP, provide architectural direction for MLOps and feature-platform capabilities, and lead or contribute to architecture RFCs, technical decisions, and design reviews.
Additionally, you'll partner with domain teams to clarify ownership and data-product responsibilities, define practical controls for data quality, observability, privacy, security, and cost management, and act as a trusted advisor and technical mentor to data engineers, platform teams, data scientists, MLOps engineers, and BI teams.
Requirements:
- 8–10+ years of experience designing and operating scalable data architectures 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 as Flink or Spark Structured Streaming
- 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
- Strong knowledge of semantic layers, business ontologies, canonical data models, knowledge graphs or metadata models, metric definitions, and semantic governance
- Expertise in data modeling, data lake and lakehouse patterns, warehouse design, data pipelines, data products, metadata, lineage, and data management technologies
- 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
- Proficiency with relevant data and platform technologies such as Spark, Airflow, dbt, Kafka, Python, SQL, CI/CD, infrastructure-as-code, and observability tooling
- Experience architecting or supporting MLOps, feature stores, online/offline data serving, or other low-latency machine-learning data systems
- Ability to establish practical frameworks for data access, stewardship, governance, privacy, security, quality, and operational accountability
- Strong understanding of reliability, scalability, performance, resilience, cost, and vendor lock-in trade-offs
- Excellent stakeholder-management, communication, facilitation, and influencing skills
- Comfortable managing risk, ambiguity, and conflict; able to make decisions and explain reasoning
- Self-sufficient and proactive, with judgment to know when to seek input and when to move forward
