About the position
We are seeking an experienced Security Practice Sales Engineer with a strong technical background in Data Discovery and Classification, Data and Cloud Infrastructure Security to join our growing team. This individual will be pivotal in driving sales efforts by offering expertise on security solutions, particularly in the areas of Data Security Posture Management (DSPM) and Cloud Security. The ideal candidate will have hands-on experience with major Cloud Service Providers (CSPs) like AWS, Azure, GCP, and Oracle. As a Security Practice Sales Engineer, this role involves working closely with the sales team to understand customer needs, demonstrate the value of security solutions, and ensure that enterprise clients' data is secure and compliant across various cloud environments and data systems. The position entails leading and engaging throughout the sales lifecycle, from discovering the customer’s requirements to writing proposals, demonstrating solutions, architecting tailored security architectures, building customer trust, and driving the proof of concept to achieve the technical win.
Responsibilities
- Partner with the sales team to drive the technical aspects of the sales cycle.
- Lead discussions around Data Discovery, DSPM, and cloud security, and present tailored security solutions to prospective clients.
- Use a deep understanding of industry best practices to help customers navigate complex situations and achieve optimal outcomes.
- Provide technical leadership in securing cloud environments, including AWS, Azure, GCP, and Oracle.
- Advise customers on best practices for securing SaaS applications and ensure that their cloud applications meet security and compliance standards.
- Lead discussions around Data Access Controls, Compliance (GDPR, HIPAA, PCI-DSS), and Data Governance best practices.
- Conduct product demos, POC’s, and presentations to clearly communicate the value of security solutions.
- Work cross-functionally with engineering, product management, and customer success teams.
- Collaborate closely with post-sales teams to ensure that customers fully realize the value of their investments.
Requirements
- Experience defining and implementing Data Discovery policies, including various techniques such as REGEX, Patterns, Dictionaries, Keywords and Exact Data Matching.
- Hands-on experience with cloud infrastructure, specifically AWS, Azure, GCP, and Oracle including identity and access management (IAM), and cloud security best practices.
- Strong knowledge of Data Security Posture Management (DSPM) and its application in cloud environments.
- Experience in securing SaaS applications and providing guidance on how to apply security best practices in cloud-based applications.
- Understanding of Data Access Controls, Data Governance, and compliance frameworks such as GDPR, HIPAA, and PCI-DSS.
- Operational experience with one or more of the following data systems: MongoDB, Cassandra, DynamoDB, HDFS, Hive, HBase, AWS S3, DynamoDB, Redshift, Azure file and blob storage, etc.
- Proficiency in Linux and Networking, with experience in containerized environments using Kubernetes is a plus.
- Proven experience in a Sales Engineer or Pre-Sales Engineer role.
- Strong verbal and written communication skills, with the ability to articulate complex technical solutions clearly.
Nice-to-haves
- Successful Sales Engineering track record with minimum of 5+ years pre-sales engineering or consulting role focusing on medium and large enterprise accounts.
- Bachelor's degree in a technical field or a related discipline.
- Relevant industry certifications with focus on cloud or security such as AWS Fundamentals, Azure, GCP, CISSP, CISM or similar.
- Experience with security tools and platforms focused on data protection, cloud security, and compliance.
- Expertise in CASB (Cloud Access Security Brokers), SASE (Secure Access Service Edge), and DLP (Data Loss Prevention) technologies.
- Experience in designing, developing, and refining RAG (Retrieval-Augmented Generation) models.
- Knowledge of vector databases and indexing methods to store and retrieve information for conversational contexts.
