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Technical Account Manager - xLake: Data & AI Platform

Acceldata· Customer Success – Solutions & Services
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Hybrid (Greater London, UK)£150k - £170kFull-timePosted 6mo ago

About the role

Customer-facing technical advisor and architect responsible for long-term customer success and value realization for Acceldata’s strategic enterprise accounts, coordinating with Sales, Product, and Engineering to translate customer priorities into multi-quarter technical success plans and enable predictable account expansion.

Responsibilities

  • Lead high-impact customer technical projects, including reference architectures, how-to guides, demo applications, and production-grade MVPs.
  • Provide technical leadership and consultation to support customers post-sale, guiding architecture, design, implementation, and adoption of Acceldata solutions.
  • Assist customers with transformational data projects, including migrations, installations, and deployments in complex environments.
  • Help overcome technical challenges across Kubernetes, cloud platforms, and data workflows, optimizing customer deployments.
  • Collaborate with internal Engineering, Product, Project Management, and Support teams to ensure successful delivery, issue resolution, and customer satisfaction.
  • Architect end-to-end solutions with a focus on scalability, performance, security, and cloud-native best practices.
  • Travel to customer sites as needed to deliver deep technical engagement and business impact.
  • Provide feedback to Product and Engineering on product gaps, areas for improvement, and feature requests that support long-term customer success.
  • Act as a trusted technical advocate for a defined set of accounts, partnering closely with Sales & Products.
  • Author and manage Joint Success Plans / Success Contracts (for next 3–4 quarters).
  • Build strong relationships with customer technical stakeholders to ensure adoption, value realization, and satisfaction.
  • Monitor technical health, usage adoption, and deployment success; proactively intervene and mitigate risks.
  • Participate in business and technical reviews, roadmap discussions, and strategic planning to align product value with customer objectives.
  • Translate technical achievements into business impact, providing actionable insights and metrics for customer leadership.

Qualifications

  • 12+ years of experience as a Solution Architect in enterprise data management or data platforms and distributed systems (Data & Control plane architectures).
  • Experience in a global SI or consulting firm, managing multiple customer engagements in a consultative role, is highly desired.
  • Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
  • Deep understanding of data pipelines and metadata management, data observability, lineage, and governance.
  • Familiarity with data quality, data catalog, and governance tools like Informatica, Collibra, Alation, Atlan, etc.
  • Experience working across AWS, Azure, or GCP environments.
  • Experience with modern cloud data platforms such as Databricks and Snowflake.
  • Hands-on experience with Apache Spark, Kafka, and other open source big data platform technologies.
  • Hands-on experience with Kubernetes and containerized workloads.
  • Strong consultative mindset with enterprise customers, with the ability to translate business outcomes into technical architectures.
  • Executive-level communication and stakeholder management skills.
  • Proven ability to manage multiple complex customers simultaneously.
  • Comfortable influencing Product and Engineering through structured inputs.

Skills

Required:Solution ArchitectureConsultative Selling / Stakeholder ManagementCloud Platforms (AWS/Azure/GCP)Databricks & SnowflakeBig Data TechnologiesKubernetes & ContainersData Governance & Metadata Management
Last updated Oct 22, 2026. Interested? Apply on the company site.

About Acceldata

AI-Native Data Management for the Modern Enterprise

Discover Acceldata, the industry’s first agentic data management platform, revolutionizing data observability, governance, optimization, and AI-driven operations. Enhance data reliability, reduce costs, and maximize efficiency across hybrid environments.

Data ObservabilityData ManagementAI-driven OperationsHybrid Environments
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