Cordoba, ArgentinaFull timeR*****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 integral team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:Bold in how we dream and innovateResponsive to feedback, challenges and opportunitiesAccountable for results and best in class outcomesVisionary in future focused problem-solvingExceptional in execution and impactPosition OverviewProofpoint is seeking a DevOps and Data Engineer to join our Data Platform team.
This is a hybrid role that sits at the intersection of platform engineering and data infrastructure-you will own the reliability, automation, and delivery of the systems that power Proofpoint's data pipelines and analytics capabilities.
You will build and maintain the cloud infrastructure (AWS-first), CI/CD pipelines, and DevOps tooling that enable data engineering teams to ship high-quality data products faster and more safely.What You'll DoYou will implement, and maintain CI/CD pipelines (GitHub Actions, Jenkins, or equivalent) for automated testing, deployment, and promotion of AWS Glue ETL jobs, Athena views, and related data infrastructure.You will build and manage cloud infrastructure on AWS using Infrastructure-as-Code (Terraform and/or AWS CDK)-covering S3 buckets, IAM roles, Glue resources, Athena workgroups, Lake Formation permissions, VPCs, and supporting services.You will own the observability stack for data pipelines: define and instrument metrics, logs, and alerts (CloudWatch, Datadog, or equivalent) to ensure SLA compliance and fast incident response.You will manage container and compute infrastructure (Docker, ECS/EKS, or Lambda) used to support data ingestion microservices and orchestration workers.You will build and maintain AWS Glue ETL jobs (PySpark) for large-scale data ingestion and transformation across the data lake on S3.You will collaborate with data engineers, data scientists, and security teams to translate requirements into scalable, automated infrastructure solutions.What You Bring to the TeamRequired2+ years of professional experience in DevOps, platform engineering, or site reliability engineering, with at least 2 years of hands-on data engineering work.Expertise with AWS core services: S3, IAM, VPC, CloudWatch, Lambda, ECS/EKS, Secrets Manager, and CloudFormation/CDK.Hands-on experience with AWS data services: Glue (ETL jobs, Crawlers, Data Catalog), Athena (workgroup management, query tuning, federated queries), and S3 (lifecycle, encryption, event notifications).
Proficiency with Infrastructure-as-Code tools-Terraform and/or AWS CDK-for managing production cloud environments.Strong CI/CD experience: designing and maintaining pipelines in GitHub Actions, Jenkins, GitLab CI,



or equivalent.Experience with container technologies (Docker) and container orchestration (Amazon ECS, EKS, or Kubernetes).
Nice to HaveKnowledge of data quality frameworks (Great Expectations, Deequ, or similar) and experience integrating them into CI/CD.Familiarity with open table formats (Apache Iceberg, Delta Lake) and lakehouse patterns.Background in cybersecurity, threat intelligence, or security-data analytics environments.Why Proofpoint?
At Proofpoint, we believe that an exceptional career experience includes a comprehensive compensation and benefits package.
Here are just a few reasons you'll love working with us:Competitive compensationComprehensive benefitsCareer success on your termsFlexible work environmentAnnual wellness and community outreach daysAlways on recognition for your contributionsGlobal collaboration and networking opportunitiesOur Culture: Our culture is rooted in values that inspire belonging, empower purpose and drive success-every day, for everyone.We encourage applications from individuals of all backgrounds, experiences, and perspectives.
If you need accommodation during the application or interview process, please reach out to .
AgileEngine is an Inc. **** company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries.
We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer-facing reporting, spend analytics, and AI features.
The mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper-intermediate English level.
WHAT YOU WILL DO
- Build and maintain the dbt pipelines, data marts, and warehouse models that serve in-app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
- Translate complicated procurement business logic into maintainable, well-tested pipeline code, and notice when a model is valid-looking but wrong.
- Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
- Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query, so that metrics and dimensions are defined once and answered consistently.




- Maintain the boundary between PII and agent/reporting-safe data.
- Build and maintain the FastAPI services that expose reporting marts to customers.
- Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
- Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
- Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
- Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
- Tune Snowflake performance and cost within the models you build.
- Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
- Share what you learn through code review and team discussion.
- Communicate clearly with engineering, product, and customer-facing teams about what the data means and what it can and can't do.
MUST HAVES
- 3+ years in analytics engineering, data engineering, or a closely related role, including hands-on work on production data pipelines that other systems or customers depended on.
- Solid dbt experience: modeling, tests, macros, and incremental models.
- Strong SQL and working Python.
- Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
- Experience with a modern cloud warehouse (Snowflake preferred).
- Experience with ingestion tooling and orchestration (Airbyte, Prefect, or equivalents).
- Familiarity with agentic coding tools and an understanding of why guardrails around their output matter.
- Deep curiosity about the business: you want to understand what the data means and how customers use it, not just how it flows.
- Software engineering discipline for analytics code: Git, code review, CI/CD.
- Can take a multi-sprint piece of work from spec to production with normal oversight, and can explain tradeoffs to technical and non-technical stakeholders.
NICE TO HAVES
- Experience building or maintaining API services on top of warehouse data (FastAPI or similar).
- Experience with a semantic layer or governed metric definitions that multiple consumers rely on, including for LLM-driven or agent-driven querying.
- Experience exposing warehouse data to customers through embedded analytics or customer-facing APIs.
- Experience with data observability tooling.
- Experience supporting customer data onboarding or implementation teams.
- Experience deploying data pipelines across multiple regions and managing PII or sensitive data.
- Knowledge of procurement and/or supply chain data models.
- Prior experience in enterprise software and startups.
PERKS AND BENEFITS
- Professional growth : Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation : USD-based pay with education, fitness, and team activity budgets.
- Exciting projects : Modern solutions with Fortune 500 and top product companies.
- Flextime : Flexible schedule with remote and office options.
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