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 real-time / streaming data processing using Apache Kafka, Amazon Kinesis, or AWS Glue Streaming ETL.
Familiarity with AWS Lake Formation for fine-grained access control and data governance.
Knowledge of Amazon Redshift or other cloud data warehouses.
Experience with data quality frameworks (Great Expectations, Deequ, or similar).
Background in cybersecurity, threat intelligence, or security analytics data.
AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect certification.
Contributions to open-source data engineering projects or publications.
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 compensation
Comprehensive benefits
Career success on your terms
Flexible work environment
Annual wellness and community outreach days
Always on recognition for your contributions
Global collaboration and networking opportunities
Our 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 accessibility@proofpoint.com.
How to Apply
Interested? Submit your application along with any supporting information- we can’t wait to hear from you!
