ABOUT NETSKOPE

Today, there’s more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security.
Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive. Visit us at Netskope Careers. Please follow us on LinkedIn and Twitter@Netskope.

POSITION SUMMARY

We are seeking a highly skilled and experienced Data Engineering Manager to be the Data Operations Chapter leader. In this role you will build, lead, and mentor a team of data engineers, devops engineers, data analysts, BI analysts and other associated roles to design, build, and maintain robust data pipelines, infrastructure, and visualizations that power our analytics and operational systems.
This is a hands-on role and you will be an integral part of the development team building data architecture and workflows, optimizing data pipelines, and implementing best practices for data management. This role requires strong expertise in modern data engineering technologies, cloud platforms, and a deep understanding of data governance, security, and compliance.

QUALIFICATIONS

Required:

• Bachelor’s degree in Computer Science, Data Engineering, or a related field (or equivalent experience).
• 7+ years of experience in data engineering or related roles, focusing on building and optimizing data pipelines and architectures.
• 2+ years of experience in a team lead or manager position.
• Proficiency in Python, Java, or Scala, along with strong SQL skills.
• Hands-on experience with AWS, Azure, or GCP and data services like S3, Redshift, BigQuery, Databricks, or Snowflake.
• Strong knowledge of data integration platforms and ETL/ELT frameworks such as Apache Airflow, dbt, Fivetran or Stitch.
• Experience with distributed data processing technologies like Apache Spark, Kafka, or Hadoop.
• Solid understanding of data modeling techniques for structured and unstructured data.
• Familiarity with data governance and compliance standards, including GDPR and CCPA.
• Excellent problem-solving abilities to troubleshoot and resolve complex data issues.
• Strong communication and collaboration skills to work effectively with cross-functional teams.
• Proven track record of leading or managing engineering teams and delivering complex projects.
• Deep understanding of CI/CD frameworks and processes for data pipelines.
• Fluent in English

Preferred:

• Master’s degree in Computer Science, Data Engineering, or a related field.
• Experience with data versioning, orchestration, and CI/CD for data pipelines.
• Knowledge of machine learning workflows and integrating data pipelines with ML models.
• Familiarity with data visualization tools (e.g., Tableau, Power BI, Looker).
• Certifications in relevant technologies, such as AWS Certified Data Analytics or Google Cloud Professional Data Engineer.

• Lead, mentor, and inspire a team of data engineers, fostering professional growth and collaboration.
• Work closely with Product Owners, stakeholders, and engineering teams to define requirements and set priorities.
• Actively engage in Agile ceremonies to ensure team alignment and successful project execution.
• Drive process, tool, and workflow enhancements to optimize team performance.
• Design, build, and maintain scalable data pipelines for batch and real-time processing.
• Architect and implement solutions like data lakes, warehouses, and real-time streaming systems.
• Optimize cloud-based data infrastructure (e.g., AWS, Azure, GCP) for performance, reliability, cost-efficiency, and security.
• Ensure data quality, consistency, and compliance through governance, automated testing, and monitoring.
• Refactor legacy pipelines and integrate CI/CD frameworks to automate workflows.
• Partner with stakeholders, data scientists, and analysts to align data solutions with business goals.
• Troubleshoot, debug, and resolve complex data issues to ensure operational excellence.
• Evaluate and integrate new technologies to stay current with industry trends and enhance capabilities.