Infrastructure & Platform Senior Specialist Solutions Engineer

Provides technical pre-sales and architecture guidance for secure, scalable Databricks deployments across major cloud platforms. The role requires strong cloud infrastructure, identity, networking, infrastructure-as-code, Python, and Spark expertise, plus the ability to engage senior customer stakeholders.

About the job

Responsibilities

Guide enterprise customers through the full lifecycle of Databricks deployments on AWS, Azure, or Google Cloud, from architecture through production deployment.
Architect secure, scalable deployments involving cloud networking, private connectivity, identity, and security compliance.
Advise on cloud infrastructure, infrastructure-as-code, networking, identity management, platform administration, and deployment management.
Demonstrate custom-built solutions and Databricks differentiation in competitive technical scenarios.
Collaborate with solution architects, data and solutions architects, forward-deployed engineers, and partners on complex customer needs.
Provide customer feedback to influence product direction.
Drive platform adoption and consumption growth within customer accounts.
Contribute customer-facing content, workshops, thought leadership, and community engagement.

Requirements

4+ years of experience in solutions architecture, technical pre-sales, or a senior hands-on technical role.
Expertise in cloud security controls, identity, encryption, vulnerability management, compliance, and identity protocols including SCIM, OAuth, SAML, and OIDC.
Experience with cloud IAM and enterprise identity services, including AWS IAM, Microsoft Entra ID, Google Cloud IAM, AWS IAM Identity Center, and Google Cloud Identity.
Expertise in enterprise cloud networking, VPC/VNet design, peering, dedicated interconnects, private connectivity, routing, performance optimization, and large-scale deployments.
Experience with high availability, disaster recovery, cluster orchestration, observability, auditing, and cloud cost management.
Hands-on infrastructure-as-code and automation experience, including Terraform and cloud-native provisioning tools.
Strong Python and PySpark/Spark coding proficiency, including live coding, debugging, and solution building.
Understanding of distributed data systems, scalable pipelines, streaming architectures, lakehouse patterns, and cloud-native data platforms.
Proficiency with the Databricks Platform or ability to become proficient rapidly.
Ability to participate in architecture discussions, whiteboarding, design reviews, and trade-off analysis with senior stakeholders.
Excellent communication skills with technical and executive audiences.
Bachelor's or master's degree in Computer Science, Engineering, or a quantitative discipline, or equivalent experience.
Willingness to travel up to 30%.

Nice to Have

Databricks certifications in data engineering, machine learning, or platform administration.
Professional-level AWS, Azure, or Google Cloud certifications.
Experience with Snowflake, cloud-native data services, Azure Synapse, or BigQuery.
Background at a data/AI company or cloud provider.