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Senior Solutions Consultant - xLake: Data & AI Platform

Acceldata· Customer Success – Solutions & Services
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Hybrid (Greater London, UK)£130k - £160kFull-timePosted 5mo ago

About the role

Senior Solutions Consultant with deep knowledge of Enterprise Data & AI Platforms, AI Fluency, Data Observability & Governance. Owns xLake Platform deployments, guiding customer infrastructure and security decisions, and delivering secure, scalable installations through consultative, hands-on engagement.

Responsibilities

  • Own cluster architecture and deployment. Lead end-to-end deployment of Acceldata ODP managed via Apache Ambari. Define cluster topology - master, slave and client node placement - sized to the customer's workload and infrastructure.
  • Prepare and harden infrastructure. Advise on and execute OS-level prerequisites on RHEL or Ubuntu: password-less SSH, DNS/NTP, firewall (iptables) rules, SELinux and PackageKit handling, and system tuning such as umask and open file descriptors.
  • Deliver secure and air-gapped deployments. Design and implement installations inside firewalled and air-gapped data centres using local mirror repositories or trusted HTTP proxy servers, working within the customer's change-control and compliance constraints.
  • Integrate backend databases. Provision and configure operational databases (Oracle, PostgreSQL, or MySQL/MariaDB), including schemas, users and JDBC drivers for core services - Ambari, Hive, Spark, Ranger and Schema Registry.
  • Extend the ecosystem via mpacks. Install, configure and troubleshoot components through Ambari Management Packs - Spark, Kafka, Flink, NiFi, Ozone, Airflow, Trino, ClickHouse, JupyterHub and MLflow.
  • Implement enterprise security. Stand up cluster security end-to-end: Kerberos integration, Apache Ranger authorization policies, SSL/TLS certificate management, LDAP/AD integration, and secure credential handling (e.g. obfuscating LDAP bind passwords via the Ambari Credential Store / JCEKS).
  • Advise and enable the customer. Run working sessions with the customer's infrastructure, security and DBA teams; document target-state architecture and handover runbooks; coordinate with Acceldata engineering and account teams to unblock issues and drive long-term adoption.
  • 8+ years of experience in customer-facing product implementation, designing and deploying performant end-to-end data architectures, and solving complex migrations and deployments.
  • Comfortable writing code in either Java, Python or Scala.
  • Consult on design and architecture; implement strategic customer projects that lead to customers' successful understanding, evaluation, and adoption of Acceldata Data Observability Cloud.
  • Good understanding of Data management concepts like Data Quality, Data Catalog and Data Governance.

Qualifications

  • Deep architectural knowledge of the Apache Hadoop ecosystem (HDFS, YARN, MapReduce, Hive, ZooKeeper).
  • Strong Linux system administration on RHEL and/or Ubuntu, with a real command of network configuration, firewall rules and system tuning.
  • Practical experience with enterprise security: Kerberos (KDC), SSL/TLS, LDAP/AD and Apache Ranger.
  • Comfort operating within the platform's runtime stack (Java and Python) for configuration and troubleshooting.
  • Excellent communication skills - able to explain hard technical trade-offs clearly to both engineers and senior stakeholders - and the autonomy to drive complex work with little supervision.

Skills

Required:Cluster architecture and deploymentOS-level infrastructure hardeningSecure and air-gapped deploymentsBackend database provisioningAmbari Management PacksEnterprise securityJava/Python/Scala codingConsulting/communicationData Management concepts
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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