dLocal is a fintech company building payment infrastructure for global businesses that serve customers in emerging markets. Its platform brings together local payment methods and currencies across more than 40 countries, with services covering payins, payouts, and real-time fraud management. By streamlining cross-border transactions, dLocal helps businesses in eCommerce, shared economy, digital media, financial services, and other sectors reach consumers across Africa, Asia, Latin America, and other high-growth markets.
Senior Data and AI Architect — dLocal, Montevideo Hybrid
Lead dLocal’s global fintech data and analytics architecture. Define governed data mesh, streaming, lakehouse, semantic, cloud, and MLOps platforms.
Description
- Set technical direction for dLocal’s data and analytics architecture
- Build a scalable, governed, and accessible data ecosystem spanning Data & AI and engineering
- Connect domain data products with real-time platforms, analytical workloads, machine-learning applications, and business consumption
- Define enterprise architectures for batch, streaming, lakehouse, warehouse, and operational data use cases
- Review RFCs and advise architects on data architecture decisions
- Drive data-mesh adoption through domain ownership, data products, federated governance, self-service platforms, discoverability, quality, and SLAs
- Create reference architectures and engineering standards covering data products, pipelines, ingestion, storage, processing, orchestration, observability, lineage, security, and access
- Design and govern streaming platforms using Kafka, Kinesis, Flink, Spark Structured Streaming, and Databricks
- Establish event-processing practices for schemas, contracts, event time, late data, idempotency, deduplication, replay, reprocessing, dead-letter handling, and freshness SLAs
- Develop cross-domain semantic layers and enterprise ontologies
- Guide cloud data platforms and lakehouse capabilities across AWS and GCP
- Set architectural direction for MLOps and feature-platform capabilities
- Lead or support architecture RFCs, technical decisions, design reviews, migration strategies, and delivery roadmaps
- Work with domain teams on ownership, product responsibilities, operational handover, quality, access approvals, and cross-domain use
- Define safeguards for data quality, observability, privacy, security, resilience, cost control, and production readiness
- Address critical architectural issues across teams and carry decisions through implementation and operations
- Mentor data engineers, platform teams, data scientists, MLOps engineers, BI teams, and engineering leaders
- Communicate the architectural vision and validate designs in production
Requirements
- Bring 8–10+ years of experience designing and operating scalable data architectures, ideally in complex enterprise or high-growth settings
- Demonstrate strong expertise in data-mesh architecture and operating models, including domain ownership, data products, federated governance, self-service platforms, contracts, quality, and discoverability
- Have deep experience with streaming and event-driven systems, including Kafka or Kinesis and a processing engine such as Flink or Spark Structured Streaming
- Show experience designing real-time and near-real-time workloads involving latency measurement, event-time semantics, late data, state, deduplication, idempotency, replay, and failure recovery
- Understand semantic layers, business ontologies, canonical models, knowledge graphs or metadata models, metric definitions, and semantic governance
- Bring expert knowledge of data modeling, lake and lakehouse patterns, warehouse design, pipelines, data products, metadata, lineage, and data management technologies
- Have experience with AWS and/or GCP data platforms; Databricks, Unity Catalog, Delta Lake, Iceberg, or comparable technologies are valuable
- Be proficient with Spark, Airflow, dbt, Kafka, Python, SQL, CI/CD, infrastructure as code, and observability tools
- Have architected or supported MLOps, feature stores, online and offline serving, or other low-latency machine-learning data systems
- Be able to establish frameworks for data access, stewardship, governance, privacy, security, quality, and operational accountability
- Understand trade-offs involving reliability, scalability, performance, resilience, cost, and vendor lock-in
- Demonstrate excellent stakeholder management, communication, facilitation, and influencing skills
- Be comfortable managing risk, ambiguity, and conflict while making and explaining decisions
- Work independently and proactively, with sound judgment about when to seek input and when to proceed
Benefits
- Flexible schedules and work arrangements that balance self-managed focus time with in-person collaboration hubs, depending on the role and location
- Opportunity to work in the fintech industry
- Employee referral bonus program
- Ability to work from anywhere while traveling for up to three months each year