Gonzalo Lencina

Design and operate event-driven AWS data platformsfor large-scalefinancial and PII workloads, withfull lifecycle ownershipfrom ingestion to delivery. Tech: PySpark, SQL, AWS Glue, Iceberg, schema evolution, Step Functions, S3, Athena, QuickSight, Cloud Formation, GitHub Actions, Docker, observability/telemetry.

• Architected event-driven pipelines on AWS Glue using Apache Spark (PySpark) with Bronze/Silver/Gold medallion layers; outputs stored as partitioned Parquet and Iceberg tables (with schema evolution) and queried via Athena.

• Applied Spark optimisations (partitioning, broadcast joins, salting, repartitioning, caching, execution graph analysis) and Iceberg table strategies to reduce compute and mitigate skew, achieving ∼30% reduction in compute hours and ∼45% lower query latency.

• Orchestrated workflows with AWS Step Functions, EventBridge, and Lambda triggers; integrated telemetry and SNS notifications for operational observability, alerting, and cost tracking via Amazon QuickSight dashboards.

• Led CI/CD adoption (GitHub Actions, CodeBuild, CodePipeline, Docker) and developed internal Python utilities for testing, SQL linting, and metadata validation to standardise deployments and improve reliability.

Led the design and development of scalable, Kubernetes-based data solutions using PySpark and Python, delivering reliable and performance-optimized pipelinesfor analytics and operations. PySpark, Python, Kubernetes (k8s/k3s), ArgoCD, Docker, Elasticsearch, Kibana, Grafana.

• Implemented and managed CI/CD workflows with ArgoCD, improving deployment consistency and reducing delivery time.

• Migrated the data infrastructure from Kubernetes (k8s) to k3s, reducing resource consumption and improving system scalability.

• Built and maintained Docker images to standardize environments and accelerate development work flows.

• Designed real-time monitoring and observability dashboards using Elasticsearch, Kibana, and Grafana, enhancing visibility and event-driven response.

Developed scalable data pipelines using Apache Spark, Scala, and Airflowfor large-scale web scraping workloads, processing millions of records dailyfrom distributed crawlers.

Tech: Scala, Spark SQL, Airflow, AWS ECS, Glue, EMR, Terraform, Athena.

• Built modular ETL components in Scala and automated ingestion workflows in Airflow, ensuring data quality and analytical consistency.

• Managed infrastructure as code with Terraform, deployed workloads on ECS, Glue, and EMR, and created Athena dashboards to enhance internal and client analytics.