Engenheiro de Dados Sênior / Especialista - AWS / Snowflake / Iceberg

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados Sênior / Especialista - AWS / Snowflake / Iceberg based in Brazil. This is a senior-level data engineering opportunity focused on designing and evolving a modern lakehouse architecture in AWS. You will act as a technical reference for the data ecosystem, building reliable and scalable pipelines across data lake and data warehouse environments. The role has a strong focus on Snowflake, Apache Iceberg, Spark, and AWS data services, with significant ownership of architecture and engineering standards. You will be responsible for balancing data quality, performance, cost efficiency, security, and governance across critical data platforms. The position combines hands-on development with technical leadership, including code reviews, mentoring, architecture decisions, and cross-functional collaboration. You will work in a dynamic, collaborative environment alongside data, product, and business teams to turn complex requirements into robust solutions. This is a fully remote opportunity suited to an autonomous engineer who enjoys solving complex technical challenges and driving continuous improvement. Design, implement, and evolve data ingestion, transformation, and delivery pipelines across AWS, including the creation and maintenance of Apache Iceberg tables on Amazon S3. Develop distributed data processing jobs using Apache Spark and PySpark through platforms such as Amazon EMR and AWS Glue, including incremental loads, upserts, MERGE operations, and historical reprocessing. Design and maintain interoperability between Snowflake and the Iceberg-based lakehouse, including external volumes, Glue Data Catalog integrations, metadata synchronization, and managed versus externally managed tables. Automate and optimize Iceberg table maintenance, including file compaction, snapshot expiration, orphan-file removal, schema evolution, partition evolution, and related housekeeping activities. Design and optimize Snowflake data models and queries, ensuring high performance and efficient utilization of computing resources. Monitor and optimize AWS and Snowflake performance and costs, including warehouse sizing, clustering, caching strategies, and data lake read costs. Establish and promote data engineering best practices around Git version control, CI/CD, data testing, documentation, lineage, and code review. Implement data governance and security controls, including Snowflake RBAC, masking policies, row-access policies, and AWS Lake Formation permissions. Diagnose and resolve performance, reliability, and availability issues across critical data environments. Serve as a technical reference for the team through code reviews, mentoring of less-experienced professionals, and documentation of architecture decisions. Collaborate with data, product, and business stakeholders to understand requirements and translate them into scalable technical solutions. Contribute proactively to the evolution of the data platform, identifying opportunities to improve architecture, automation, reliability, and engineering efficiency. Requirements At least 5 years of proven experience working with AWS Cloud in production environments. At least 3 years of hands-on Snowflake experience , including data modeling, query optimization, warehouse management, and cost optimization. Proven production experience with Apache Iceberg , preferably 2+ years, including partitioning, schema evolution, snapshots, time travel, MERGE operations, maintenance, and file compaction. Strong experience with Apache Spark at scale , particularly PySpark, including job tuning and troubleshooting of skew and shuffle-related issues. Solid knowledge of AWS data services, including S3, Glue ETL, Glue Data Catalog, EMR, Athena, Lambda, and Step Functions . Advanced SQL skills, with the ability to develop complex queries and optimize workloads involving la