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Senior Solutions Consultant - Hadoop/Data Platform

Acceldata· Customer Success – Solutions & Services
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Hybrid (California)$140k - $160kFull-timePosted 3mo ago

About the role

Senior Solutions Consultant with deep Hadoop knowledge, acting as principal technical owner of Open Data Platform deployments, guiding customer infrastructure, security, and database teams to architectural decisions and ensuring secure, scalable deployments.

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 hardeningAir-gapped deployment designDatabase provisioning and configurationAmbari Management PacksEnterprise security implementationsJava, Python or Scala coding
Nice to have:Data management concepts

Benefits

  • Competitive salary: $140,000 - $160,000 a year
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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