Jobs at Solvd, Inc.

The Product Data Scientist partners with credit card and banking product teams to identify growth opportunities, define product metrics, analyze consumer behavior, design and evaluate A/B tests, and build predictive models. They use SQL, Python, Databricks, and BI tools to deliver analyses, dashboards, automated workflows, and actionable recommendations that influence product strategy and consumer-facing features.

Owns Mac-first desktop support, Microsoft 365 and Entra ID administration, SaaS access, onboarding and offboarding, hardware lifecycle, and helpdesk escalations. Leads MDM evaluation and migration, manages security baselines and identity integrations, supports remote employees globally, mentors junior staff, and automates and documents recurring processes using scripting and workflow tools.

Design and build AI-powered customer support features, including agentic research workflows, MCP servers, RAG pipelines, and OpenAI integrations. Develop supporting frontend and backend services, optimize production systems for cost, latency, and accuracy, and contribute to architecture and technical standards. Collaborate across product, design, architecture, and delivery teams while leading end-to-end implementation of enterprise-scale distributed systems.

Implement and operate multi-region AWS database infrastructure for a Tier-1 payments platform. Responsibilities include DynamoDB Global Tables replication, failover and failback automation, Terraform or CDK infrastructure, observability, disaster recovery testing, KMS and IAM configuration, CDC pipelines, data migration cutovers, and application resilience patterns. The role also requires runbook and design documentation, collaboration with data, SRE, and application teams, and support for regulated financial-services workloads.

Drive new enterprise logo acquisition for AI, cloud, digital, data, and engineering services. Generate pipeline through outbound prospecting, campaigns, events, and partnerships; own the full sales cycle from discovery through contract closure; engage executive stakeholders; negotiate commercial agreements; maintain CRM and forecast discipline; and manage new accounts for six months after closing before handoff to a Client Partner.

Build and maintain backend services powering food search and logging. Tune Elasticsearch performance across indexing, query evaluation, relevance, cluster management, and monitoring. Develop search-quality metrics and experiments, evaluate data technologies, and collaborate with engineering and product teams. Write production Scala services while working with SQL and Python, large-scale data infrastructure, databases, Kafka, cloud platforms, and AI-powered development tools.

Own end-to-end IT security for a remote/hybrid, cloud-native organization. Responsibilities include architecting identity-centric security, managing Conditional Access, automating onboarding and offboarding with scripts, securing macOS and Windows endpoints and SaaS platforms, enforcing Zero Trust, SSO, and DLP policies, using AI tools, and maintaining compliance with SOC 2, HIPAA, and GDPR.

Leads cloud, infrastructure, data, security, and AI architecture across AWS, Azure, Kubernetes, and infrastructure-as-code environments. Defines reference architectures, landing zones, data platforms, GenAI production patterns, security controls, cost models, and operational standards. Shapes client engagements, technical scope, and roadmaps; guides delivery through design reviews and mentoring. Requires hands-on production architecture experience, strong security and governance expertise, and the ability to operate effectively amid ambiguity.

Senior Data Engineer responsible for migrating a production SaaS ETL and streaming platform to a Snowflake Native Application. The role includes redesigning ingestion, translating legacy transformations, implementing cross-account multi-tenant isolation, rebuilding core data models, addressing security and technical debt, validating production-scale patterns, and documenting migration decisions. Candidates need strong Snowflake, production ETL migration, streaming, cloud services, security, and multi-tenant SaaS experience.

Own the modernization of a manual, per-customer release process into automated, parameterized CI/CD. Build secrets-management infrastructure, infrastructure-as-code across environments, and observability for distributed systems. Maintain multi-platform build and release tooling, strengthen access controls and logging, and support a self-serve platform migration. The role requires production ownership of CI/CD, secrets rotation and scoping, Terraform or equivalent, distributed-system observability, and multi-platform packaging and signing.

Design, maintain, and execute automated and manual tests for iOS and Android applications. Build mobile automation frameworks with XCUITest and Espresso, perform functional and exploratory testing, investigate defects, create test plans and reports, integrate tests into CI/CD pipelines, and collaborate with engineering and product teams throughout Agile development cycles.

Designs and develops high-performance Go applications, background services, telemetry systems, and distributed clients for Windows and macOS. Responsibilities include concurrency, secure data transmission, encrypted persistence, cloud integrations, resilience, performance optimization, testing, monitoring, packaging, and deployment. Provides technical leadership through architecture and design reviews, code reviews, troubleshooting, and mentoring.

Designs and owns the end-to-end architecture of a Snowflake Native App for Marketplace publication. Responsibilities include application packaging, cloud-agnostic architecture, data flow and security design, ETL migration tooling evaluation, provider account setup, versioning, release architecture, and leading Snowflake Security Review documentation and compliance activities.

Own full-stack product features from design through release. Design and maintain GraphQL APIs with Apollo Server, backend services, data access and caching layers, database schemas, and React interfaces using TypeScript. Write unit and Cypress end-to-end tests, participate in agile ceremonies, and collaborate within a fast-moving product team. Preferred experience includes Next.js, AI-assisted development tools, Kubernetes, Terraform, and Helm.

The Application Security Engineer will define and validate security controls for a high-traffic digital news platform. Responsibilities include threat modeling, architecture reviews, authentication and authorization assessment, API and integration security, secrets management, vulnerability and penetration testing coordination, encryption validation, security monitoring, GDPR privacy engineering, vendor assessments, and remediation documentation.

Configure and develop CMS and DAM capabilities for a greenfield, video-first digital news platform. Responsibilities include content modeling, editorial workflows, publishing, permissions, taxonomy, rights management, asset lifecycle workflows, and integrations with video, search, CDN, and distribution services. The role also supports testing, migration, training, governance, launch stabilization, and long-term platform operations.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Designs and delivers production-grade GenAI and agentic AI solutions for enterprise clients. Leads discovery, solution architecture, rapid prototyping, technical presales, evaluation, integration, and MVP or first-production delivery. Works with LLMs, enterprise data, workflows, orchestration, vector databases, cloud AI services, and tool-calling systems. Translates ambiguous business problems into validated solutions while addressing cost, security, compliance, observability, responsible AI, adoption, and production readiness.

Deliver end-to-end data science projects using machine learning and deep learning, collaborate with stakeholders, and support client engagement through analytical planning and solution design.

Deliver end-to-end data science projects, collaborate with stakeholders, present analytical findings, support presales activities, and shape the Data Science practice.