Why Join d Local?

d Local 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.

You'll work alongside 1,300+ teammates from 40+ nationalities and tackle general challenges from day one.Why Join d Local?

d Local 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.

You'll work alongside 1,300+ teammates from 40+ nationalities and tackle global challenges from day one.We are looking for a Data Architect to provide the technical direction for d Local'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.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.Review and advise other architects on the data aspects of their RFCs, helping ensure consistency with enterprise data principles, governance standards, and architectural direction.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.Provide oversight on operational SLAs, including latency, cost, quality, freshness, reliability, and production performance.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?processing patterns, including schema and data contracts, schema registries, event?Establish patterns that allow semantic models to serve analytics, operational applications, machine learning, and AI use cases without creating duplicated or contradictory definitions.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.platform capabilities, including batch and online features, model?latency data paths, model/data lineage, monitoring, and governance.Partner with domain teams to clarify ownership, data?product responsibilities, operational handover, quality accountability, access approval, and cross?Define practical controls for data quality, observability, privacy, security, resilience, cost management, and production readiness.Act as a trusted advisor and technical mentor to data engineers, platform teams,



data scientists, MLOps engineers, BI teams, and engineering leaders.8-10+ years of experience designing and operating scalable data architectures, preferably in complex enterprise or high?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.driven architectures, including Kafka or Kinesis and one or more processing engines such as Flink or Spark Structured Streaming.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?Also, you can check out our webpage, Linkedin and Youtube for more about d Local!

If you would like more information about how your data is processed, please contact us.

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📌 Data Engineer Senior AWS y Airflow (Madrid)
🏢 dLocal
📍 Madrid