Sobre

• 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.
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Accountabilities

• 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 large volumes of data.
• Strong understanding of data modeling and Data Warehouse, Data Lake, and Lakehouse architectures.
• Proficiency in Python for automation and data engineering tasks.
• Experience with data pipeline orchestration tools such as Airflow, Step Functions, dbt, or equivalent technologies.
• Strong familiarity with Git, version control practices, automated testing, and code review processes.
• Technical English proficiency sufficient to read and understand technical documentation.
• Ability to work autonomously and drive complex technical deliveries with limited supervision.
• Strong analytical and problem-solving skills, with the ability to investigate and resolve complex data engineering challenges.
• Clear communication skills and the ability to collaborate effectively with business stakeholders and multidisciplinary teams.
• A proactive, collaborative mindset and willingness to propose and implement technical improvements.
• Mandatory: proven professional experience with AWS for at least 5 years and Snowflake for at least 3 years.
• Mandatory: ability to present a PowerPoint case demonstrating practical experience and work with Snowflake.

Nice-to-have qualifications

• SnowPro Core or Advanced certification.
• AWS certifications such as Solutions Architect, Data Engineer, or Data Analytics.
• Experience with Terraform or other Infrastructure as Code technologies.
• Production experience with dbt.
• Experience with open catalogs such as Glue Data Catalog, Polaris/Open Catalog, or Unity, particularly in multi-engine environments.
• Experience with streaming technologies such as Kinesis, Kafka/MSK, or Snowpipe Streaming.
• Familiarity with data observability and quality tools such as dbt tests, Great Expectations, or Monte Carlo.
• Benefits
• Fully remote work model within Brazil.
• Opportunity to work with modern AWS, Snowflake, Apache Iceberg, Spark, and Lakehouse technologies.
• Technical ownership of a strategic data engineering ecosystem.
• Opportunity to act as a technical reference, mentor other professionals, and influence architecture decisions.
• Continuous exposure to complex data, cloud, analytics, and AI-driven technology challenges.
• Collaborative environment with multidisciplinary teams and opportunities for continuous professional development.
• Work on innovative technology solutions designed to create measurable business impact.

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How Jobgether works

• We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
• We appreciate your interest and wish you the best!
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