Staff Data Engineer

Role overview

Lead the design and operation of large-scale data systems that support complex regulatory workflows. The role spans the data lifecycle, from ingestion and validation through modeling and downstream delivery, with a focus on correctness, reliability, performance, and cost. You will set technical direction, improve engineering standards across teams, and mentor senior engineers.

Responsibilities

- Design multi-tenant batch and streaming pipelines, including ingestion, validation, domain modeling, file generation, and submissions. - Establish standards for modularity, data contracts, testing, and reproducible workflows. - Optimize Python and Spark workloads to improve latency, cost, and service objectives; build in retries, idempotency, and back-pressure. - Own production data infrastructure on AWS, including operational resilience, security, monitoring, and cost governance. - Set the roadmap for workload isolation, schema evolution, partitioning, compaction, and caching; improve lineage and data quality visibility. - Lead design reviews, mentor experienced engineers, and work with product and subject matter experts to turn ambiguous needs into production data products.

Requirements

- 8–10+ years in data engineering or a related field, with experience delivering and operating large-scale, business-critical systems. - Expert Python and SQL skills, plus strong Spark or PySpark experience, including performance tuning and cost optimization. - Deep experience with ETL/ELT and streaming pipelines, orchestration such as Airflow, and data contracts and testing approaches. - Strong data modeling skills across normalized, dimensional, wide-table, and domain-oriented models, including schema evolution and multi-tenant governance. - Production experience with AWS data services such as S3, Glue, EMR, and RDS/Aurora, as well as IAM and KMS. - Technical leadership across architecture, engineering standards, automation, reliability, on-call operations, and incident response.

Nice to have

- Experience with Iceberg or Delta Lake, dbt, data quality frameworks, or lineage tools. - Exposure to Kafka, Kinesis, change data capture, containers, Kubernetes, and Terraform. - Experience integrating data platforms with backend services or APIs, or applying machine learning to data quality and workflow automation.

Benefits and work setup

The role is listed as remote in Argentina or Brazil, or hybrid in Montevideo, Uruguay. The description also indicates that equivalent education, training, and relevant experience may substitute for a formal degree.