Staff Data Scientist, Lead Generation

Launch Potato; Buenos Aires; On-site; ARS 2,500,000 - 4,000,000; Full time

Job summary

Proofpoint is seeking a Staff Data Engineer in Argentina to design, build, and operate large-scale data infrastructure powering cybersecurity analytics. You will work with AWS-based pipelines, data lakes, and governance to deliver reliable data assets for product decisions.

You will collaborate with data scientists, ML engineers, and product managers to translate requirements into robust data models, pipelines, and scalable solutions while mentoring junior engineers.

Qualifications

• 8+ years of professional software or data engineering experience.
• 4+ years focused on cloud data platforms.
• Deep expertise with AWS data services (S3, Glue, Athena).
• Strong Python and PySpark/ Spark skills.
• Solid SQL fundamentals.
• Excellent communication skills with stakeholders.
• Experience mentoring engineers and leading design discussions.
• Bachelor's degree in CS, Engineering, or related field.

Responsibilities

• Design and implement scalable data pipelines on AWS Glue (PySpark) to ingest and transform petabyte-scale datasets stored in Amazon S3.
• Maintain cloud data lake architecture on S3 with partition strategies, file formats (Parquet, ORC, Delta), and data-catalog schemas in AWS Glue Data Catalog.
• Establish data quality standards and SLA-driven alerting to ensure data reliability.
• Define engineering best practices: CI/CD for data pipelines, Infrastructure-as-Code (Terraform/CDK).
• Mentor junior engineers and conduct design reviews, guiding the technical roadmap.
• Collaborate with data scientists, ML engineers, and product managers to translate requirements into data models and pipelines.
• Champion data governance, lineage tracking, and privacy-by-design principles across data assets.
• Troubleshoot production incidents, perform root-cause analysis, and drive preventive improvements.
• Contribute to ADRs and technical documentation.

Skills

AWS data services

S3

Glue

Athena

Python

PySpark

SQL

Communication

Mentoring

Leadership

Education

Bachelor's degree in CS/Engineering

Tools

Terraform

CDK

Job description

Cordoba, Argentina

Full time

R14749

About Us

Proofpoint is a global leader in human- and agent-centric cybersecurity. We protect how people, data, and AI agents connect across email, cloud, and collaboration tools. Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows. Our mission is simple: safeguard the digital world and empower people to work securely and confidently. Join us in our pursuit to defend data and protect people.

How We Work

At Proofpoint you'll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:

• Bold in how we dream and innovate
• Responsive to feedback, challenges and opportunities
• Accountable for results and best in class outcomes
• Visionary in future focused problem-solving
• Exceptional in execution and impact

Position Overview

Proofpoint is looking for a Staff Data Engineer to join our growing Data Platform team. In this senior individual-contributor role you will design, build, and operate the large-scale data infrastructure that underpins Proofpoint's cybersecurity products and analytics. You will work across the full data lifecycle - ingestion, transformation, orchestration, and delivery - using modern cloud-native tooling on AWS. You'll partner with data scientists, product engineers, and business stakeholders to turn raw data into reliable, governed, and performant data assets that drive product decisions and customer value.

What You'll Do

• Design and implement scalable, reliable data pipelines using AWS Glue (PySpark and Glue Studio) to ingest and transform petabyte-scale datasets stored in Amazon S3.
• Build and maintain our cloud data lake architecture on S3-defining partition strategies, file formats (Parquet, ORC, Delta), and data-catalog schemas in AWS Glue Data Catalog.
• Establish and enforce data quality standards-implement validation frameworks, anomaly detection, and SLA-driven alerting to ensure data reliability.
• Define and drive engineering best practices: code reviews, CI/CD for data pipelines, Infrastructure-as-Code (Terraform/CDK), and DataOps principles.
• Serve as a technical leader and mentor-conduct design reviews, guide junior engineers, and influence the team's technical roadmap.
• Collaborate closely with data scientists, ML engineers, and product managers to translate business requirements into robust data models and pipeline specifications.
• Champion data governance, lineage tracking, and privacy-by-design principles across all data assets.
• Troubleshoot and resolve production incidents, perform root-cause analysis, and drive preventive improvements.
• Contribute to architecture decision records (ADRs) and technical documentation.

What You Bring to the Team

Required

• 8+ years of professional software or data engineering experience, with at least 4 years focused on cloud data platforms.
• Deep expertise with AWS data services: S3 (lifecycle policies, event notifications, encryption), AWS Glue (ETL jobs, Crawlers, Data Catalog, Glue Studio), and Amazon Athena (query optimization, workgroups, federated queries).
• Strong Python programming skills; proficiency writing production-grade PySpark or Spark (Scala) for large-scale data transformation.
• Solid SQL fundamentals-ability to write and tune complex analytical queries.
• Excellent communication skills-able to present technical trade-offs clearly to both engineering and non-engineering stakeholders.
• Proven experience mentoring engineers and leading technical design discussions.
• Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

Nice to Have

• Experience with