Technical Support Manager - Hadoop Observability & Open Data Platform

The Manager - Technical Support at Acceldata is a crucial role to ensure that our customers are satisfied with our products and services. TSM responsibilities include supporting customers as they transition from sales prospects, providing ongoing support during various stages of the product lifecycle, liaising internally with R&D, Accounts, and Sales to provide a cohesive customer experience, and building close relationships that are mutually beneficial for customers and Acceldata.

This position will report to the Director, Global Technical Support. You will manage the team that directly interacts with our existing clients, identifies ways that Acceldata’s service can improve their business and be responsible for demonstrating and driving that value towards customers globally. The Technical Support Manager will establish excellent cross-functional relationships with other Acceldata leaders to ensure that all Acceldata products & services are deployed in a manageable and supportable way.

Managing a growing team of talented Support engineers, ensuring they are productive, happy and motivated team players.

Engage with customers and internal stakeholders to address any escalations.

Identify and work on any enhancements in the Support Processes and Products.

Promote contributions to Acceldata Product Roadmap that help make Support Engineers' troubleshooting and diagnostics easier.

Responsible for talent management within the team, including hiring the right talent, performance evaluation, feedback & training.

Strong Data Platform, Compute, Data Lineage & Quality, Hadoop & analytical skills and a passion for problem-solving.

Prior Experience with Kubernetes, Spark, and SQL is preferred.

Leadership & Team Management

• Lead and develop a globally distributed technical support team in a 24/7/365 environment. Provide structured coaching through 1:1s, case reviews, and enablement, integrating AI-assisted insights into reviews while ensuring outputs are validated to drive technically grounded responses, consistent case progression, and empathetic customer communication.
• Oversee hiring, onboarding, and professional development across locations, building strong skills in technical troubleshooting, customer communication, and effective use of AI tools in support workflows. Ensure employees have clear career pathways that recognize technical excellence, customer impact, and AI-augmented performance.
• Lead with clear context and a global mindset, delegating effectively to drive ownership and accountability, removing barriers, and keeping teams focused on shared goals connected to business outcomes.
Operational Excellence & AI-Driven Insights
• Maintain end-to-end ownership of customer incidents and model high-quality case handling by leading from the front on critical escalations, blending deep technical reasoning with AI tools to accelerate diagnosis while maintaining accuracy and customer trust.
• Use data-driven and AI-assisted strategies to optimise efficiency. Monitor KPIs (case ownership, backlog, response times, resolution SLAs) and translate insights into clear, actionable plans for the team.
• Ensure responsible AI use by setting standards for validating outputs before they reach customers, defining guardrails for privacy, security, and data use, and reviewing AI-related metrics (adoption, quality, error rates).
Cross-Functional Collaboration
• Build strong partnerships with Product, Engineering, Sales, Professional Services, and internal AI/platform teams to ensure Support's needs are represented in product and tooling decisions.
• Collaborate with escalation management and partner teams to resolve complex issues, using operational and AI-generated insights to identify patterns and address root causes.
• Drive knowledge sharing and AI enablement by encouraging AI-assisted co-authoring of knowledge content, and transform customer feedback into actionable service improvements, including opportunities to refine support tooling and AI models.
Change Management & AI-Driven Innovation
• Identify operational gaps and partner with cross-functional teams to improve case-handling frameworks, tooling, and training resources, including AI assistants embedded in support workflows.
• Champion organisational change and the scaling of new AI capabilities. Engage constructively in discussions, align on decisions, and execute. Lead pilots and rollout plans for new tools, defining success metrics, monitoring impact, and ensuring sustainable adoption.
• Foster a collaborative culture of excellence and experimentation, where engineers and managers are encouraged to propose, test, and measure AI-driven improvements to how they work.