Data-AI Architect

dLocal

. Provide technical direction for dLocal’s data and analytics architecture .

Posted 9/21/2026full-timeMontevideo • UruguaySeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing scalable data architectures, including data-mesh principles and streaming architectures. Proficient in cloud data platforms, data governance, and MLOps, with strong stakeholder management and communication skills.

Highest-signal resume keywords

Data-Mesh ArchitectureStreaming ArchitecturesCloud Data PlatformsData GovernanceMLOps

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Hard Skills

Data Architecture DesignData ModelingData Pipeline DevelopmentEvent-Driven ArchitectureSemantic Layer DesignData Quality ManagementReal-Time Data ProcessingData Lakehouse PatternsMetadata ManagementData Product Development

Soft Skills

Stakeholder ManagementCommunicationFacilitationInfluencingConflict Resolution

Tools & Technologies

KafkaKinesisFlinkSpark Structured StreamingDatabricksAWSGCPAirflowDbtCI/CD

Industry Keywords

Data GovernanceData QualityData PrivacyData SecurityOperational Accountability

Tech Stack

Tools & technologies

AirflowAWSCloudGoogle Cloud PlatformKafkaPythonSparkSQLUnity

About the role

Key responsibilities & impact

Provide technical direction for dLocal’s data and analytics architecture
Shape a scalable, governed, and highly consumable data ecosystem across Data & AI and engineering
Connect domain-owned data products, real-time platforms, analytical workloads, machine-learning use cases, and business-facing data consumption
Define and evolve enterprise data architectures across batch, streaming, lakehouse, warehouse, and operational use cases
Review and advise architects on data aspects of RFCs
Lead adoption of data-mesh principles, including domain ownership, data as a product, federated governance, self-serve platforms, discoverability, quality, and data-product SLAs
Establish reference architectures and engineering standards for data products, pipelines, ingestion, storage, processing, orchestration, observability, lineage, security, and access management
Design and govern streaming architectures using Kafka, Kinesis, Flink, Spark Structured Streaming, and Databricks
Define event-processing patterns including schemas, data contracts, event-time processing, late-event handling, idempotency, deduplication, replay, reprocessing, dead-letter flows, and freshness SLAs
Shape semantic layers and enterprise ontologies across domains
Guide cloud data platforms and lakehouse capabilities across AWS and GCP
Provide architectural direction for MLOps and feature-platform capabilities
Lead or contribute to architecture RFCs, technical decisions, design reviews, migration plans, and implementation roadmaps
Partner with domain teams on ownership, data-product responsibilities, operational handover, quality accountability, access approval, and cross-domain consumption
Define controls for data quality, observability, privacy, security, resilience, cost management, and production readiness
Resolve critical architectural issues across teams and ensure decisions reach implementation and operation
Mentor data engineers, platform teams, data scientists, MLOps engineers, BI teams, and engineering leaders
Communicate architectural vision while validating designs in production

Requirements

What you’ll 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 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 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 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
Ability to manage risk, ambiguity, and conflict; make decisions and explain the reasoning behind them
Self-sufficient and proactive, with judgment to know when to seek input and when to move forward

Benefits

Comp & perks

Flexible schedules and flexible work arrangements combining self-managed focus time with in-person connection in collaboration hubs, depending on role and location
Fintech industry environment
Referral bonus program
Work From Anywhere while traveling for up to 3 months every year