Role: Big Data Support Engineer

Job Title: Big Data Support Engineer

Location: PH

Department: Support

Reports to: Head of Support

We are seeking a Technical Support Agent who will play a critical role in maintaining high customer satisfaction by ensuring timely and effective resolution of client issues. This role is essential for Tookitaki's support services, catering to both on-premise and cloud-hosted (CaaS)clients. You will work closely with cross-functional teams to manage daily support issues, adhere to SLAs, and contribute to the continuous improvement of our customer support processes.

The ideal candidate will have a strong understanding of Tookitaki's product and tech stack, the ability to triage issues effectively, excellent client management skills, and fluency in English. Knowledge of Cantonese is a plus.

Key Responsibilities:

Client Support Management

Handle and triage tickets related to incidents, service requests, and change requests via the Freshworks platform.
Provide technical support for Tookitaki's CaaS and on-premise clients post implementation, ensuring issues are resolved within SLA timelines.
Maintain ownership of client issues, ensuring resolutions align with SLAs and meet client expectations.

Collaboration and Escalation

Collaborate with Tookitakiʼs Product Engineering and Infrastructure teams to escalate unresolved issues, secure workarounds, or deliver fixes for P1 to P4 tickets.
Act as a bridge between Services (onboarding team) and Support, ensuring a seamless transition when clients go live.

Technical Expertise and Triage

Triage technical issues effectively by diagnosing the problem, identifying the root cause, and determining the appropriate resolution path.
Develop a deep understanding of Tookitaki's product architecture and tech stack AWS, Big Data technologies like Hive, ElasticSearch, Kubernetes, etc).

Client Management and Communication

Build and maintain strong relationships with clients, demonstrating excellent communication skills and a customer-first approach.
Clearly explain technical resolutions to non-technical stakeholders, ensuring transparency and trust.

Continuous Improvement and Documentation

Maintain thorough documentation of all support tickets, including actions taken and lessons learned, in the Freshworks platform.
Proactively suggest process improvements to enhance support efficiency and client satisfaction.

Rotational Shifts and Availability

Participate in rotational shifts and ensure availability during defined upgrade windows (e.g., second and fourth Saturdays) to support both infra-wide updates and tenant-specific changes.
Ensure 24/7 availability as part of the teamʼs support structure for critical escalations.

Qualifications & Skills

Own customer incidents, service requests, and change requests through Freshworks or equivalent ticketing platforms.

Troubleshoot production issues affecting Spark jobs, Kubernetes workloads, Kafka pipelines, Elasticsearch clusters, APIs, and cloud infrastructure.
Ensure all customer issues are resolved within SLA while maintaining high customer satisfaction.
Perform root cause analysis (RCA) and document preventive actions.
Monitor health and performance of Spark applications running on Kubernetes.
Investigate failed Spark jobs, executor failures, pod crashes, resource contention, and scheduling issues.
Monitor Kafka topics, brokers, consumer groups, lag, and message delivery.
Support Elasticsearch cluster health, indexing pipelines, shard allocation, and search performance.
Perform production validations after deployments and infrastructure upgrades.
Participate in planned maintenance activities, upgrades, and release support.
Diagnose issues across the complete cloud-native stack including Apache Spark on Kubernetes, Apache Kafka, Kubernetes (Pods, Deployments, Services, ConfigMaps, Secrets), Docker Containers, AWS Infrastructure, Elasticsearch, Airflow Workflow Orchestration, Hive / Trino / SQL-based Data Processing, REST APIs and Microservices, Linux-based Production Systems.
Work closely with Product Engineering to identify software defects and Infrastructure teams to resolve

platform-related issues.

Coordinate with Product Engineering for bug fixes and product improvements.
Work closely with Infrastructure teams during production incidents.
Support onboarding teams during production go-live and customer transition.
Participate in Major Incident Management (P1/P2).
Provide timely updates to customers during incidents.
Communicate technical issues in a clear and business-friendly manner.
Maintain ownership until issue closure.
Prepare incident summaries, RCA documents, and customer communications.
Create and maintain SOPs, troubleshooting guides, and knowledge base articles.
Identify recurring issues and recommend automation opportunities.
Improve monitoring, alerting, and operational processes.
Contribute to platform reliability and operational excellence initiatives.
Participate in 24x7 production support rotation.
Provide support during scheduled maintenance windows, infrastructure upgrades, and customer go-lives.
Support weekend deployment activities when required.

Technical Skills

Apache Spark (Spark on Kubernetes preferred) ,Kubernetes, Apache Kafka, Elasticsearch

AWS Services (EC2, EKS, S3, IAM, CloudWatch), Docker, Airflow, Hive / Trino, Linux Administration

Prometheus, Grafana, Kibana, Elasticsearch Monitoring, CloudWatch, Kubernetes Logging & Monitoring

Log Analysis and Production Diagnostics, Strong SQL knowledge, Understanding of distributed systems architecture, Knowledge of networking fundamentals.

Experience using REST APIs

Familiarity with Git and CI/CD concepts

Ability to analyze application logs and distributed system failures

Understanding of resource management, autoscaling, and Kubernetes scheduling

Excellent written and verbal English communication

Strong stakeholder management

Ability to communicate technical concepts to non-technical users

Experience managing customer escalations

Mandarin (spoken and written) is preferred

Cantonese is an added advantage

Strong analytical and troubleshooting skills

Ability to work under pressure during production incidents

Prioritize incidents based on business impact

Perform structured RCA and recommend preventive measures

Experience working with SLA-driven support environments

Familiarity with ITIL incident management processes

Strong documentation practices

Experience using Freshworks, Jira, ServiceNow, or similar platforms

Experience

3-6years of experience supporting large-scale production systems. Experience supporting cloud-native applications

running on Kubernetes. Hands-on production support experience with Spark,Kubernatives, Kafka, Elasticsearch, and AWS.

Experience in Financial Services, FinTech, RegTech, SaaS, or Big Data platforms is highly desirable.

Big Data Support Engineer L1