Cloud Infrastructure Automation Engineer

Who are we? Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines. Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008. About the Role As a Platform Engineer, you will serve as the technical and strategic leader for the development and evolution of the Engineering Platform. You will drive the platform's roadmap execution, align it with organizational objectives, and ensure its scalability, reliability, and extensibility. This is a high-impact role requiring technical depth, architectural foresight, and leadership in guiding teams. Key Responsibilities Strategic Leadership Define the architecture and long-term vision for the platform team , aligning it with our roadmap and business objectives. Collaborate with platform architects and product owners to define long-term vision for self-service datastore provisioning and lifecycle management. Drive engineering initiatives that enable consistent, secure, and scalable management of diverse datastores. Align datastore platform evolution with broader enterprise infrastructure and data management strategies. Roadmap Execution Translate strategic objectives into a clear technical roadmap with incremental deliverables for self-service datastore capabilities. Work with cross-functional teams to prioritize enhancements, integrations, and automation features while ensuring alignment with data platform goals. Deliver high-impact automation to reduce provisioning time, increase reliability, and improve developer experience. Technical Execution Design and implement platform APIs, Infrastructure-as-Code templates, and orchestration pipelines for automated creation, scaling, and management of datastores. Integrate provisioning workflows with internal service catalogs, CI/CD pipelines, and monitoring solutions. Ensure consistent configuration management and environment parity across development, staging, and production datastores. Troubleshoot and resolve performance, reliability, and integration issues. Mentorship & Team Enablement Guide junior engineers and peers in best practices for infrastructure automation, datastore management, and platform reliability. Conduct code reviews, architectural discussions, and knowledge-sharing sessions focused on modern platform engineering practices. Security, Compliance, and Governance Implement automated guardrails for datastore provisioning ensuring compliance with data security policies, encryption standards, and regulatory requirements Maintain role-based access controls, audit logging, and vulnerability remediation processes for datastore services. Work closely with InfoSec teams to proactively address security risks and ensure platform resilience. Optimization & Scalability Architect datastore solutions to minimize operational cost while ensuring high availability and fault tolerance. Lead initiatives to optimize performance of large-scale and multi-tenant datastore services through sharding, clustering, caching, and replication strategies. Continuously assess new datastore technologies, infrastructure patterns, and scaling approaches to keep the platform future-ready. Qualifications 6–8 years of experience in platform engineering, DevOps, or infrastructure automation roles, preferably working with data platforms. T echnical Expertise: Deep expertise with AWS for provisioning, security and management of data infrastructure. Advanced experience writing and managing Terraform infrastructure-as-code Strong programming and scripting abilities in Python, Go, Bash, or Java for automation and custom tooling. Deep experience with containerization and orchestration (Docker, Kubernetes), and integrating platform workflows with CI/CD pipelines. Operational knowledge of data technologies including MongoDB, Kafka, Elasticsearch, Cassandra, S3, RDS, and SFTP, including configuration, scaling, and backup strategies. Leadership Skills: Proven ability to technically guide the teams and dive into the solving problems and ready for getting their hands dirty. Strong collaboration and communication skills for working across functional teams and stakeholders.