Big Data Administrator
Summary of Position
He/She will be a technical L3 resource for all Big Data services . As such, he/she will provide support for all production support activities within the Big Data team in Singapore. He/She is accountable for the overall health and stability of the technical solutions within his scope. He/She has to effectively work with technical peers such as architects, peer experts and project teams on technology road maps and projects. He/She will work with L3/Service Manager to gain control over the scope of technical activities, develop best practices and gain knowledge over all aspects of support.
The Big Data team operates the RUN activities and delivers projects for its infrastructure scope covering all Big Data associated technologies.
Job detailed description
As a L3 resource of his /her team, he /she:
Takes up technical tasks and also manages delegation for technical issues within the team,
animates the team to encourage collaboration and sharing of best practices,
supports new technologies and leverages them to provide consistency of service across streams,
proposes service improvements for all Big Data services supported throughout the organization,
documents, reviews, maintains and shares relevant technical information within the team
provides technical knowledge, supports services both proactively and reactively to maintain the availability and reliability of system infrastructure in accordance to the SLA,
works in line with policies based on LEAN-CA-CIB best practices,
Actively engages during any high severity issue and drives for issue resolution.
reviews technology changes to identify potential risks,
As an experienced professional in Big Data Services, he/she:
supports his/her team during diagnosis when technical issues rise in his/her scope of expertise,
is aware of the global IT structure so that he/she anticipates interrelationships within the organization,
engages with technical peer, Development team, Service managers, Architect and project teams on technology roadmap and projects,
facilitates transformation projects and suggest future directions for new areas of improvement and change,
guarantees the production readiness and license to operate of new projects and solutions
is available and able to drive technically, any complex or high severity incidents that occur within the scope of their role
actively engages to understand new technologies and technology trends and reviews them with a view to incorporating them into CACIB operations
actively assists in identifying the most technical skilled candidates for open roles,
technically coach and develop partner resources to improve quality and productivity,
Candidate profile
Mandatory track record
Excellent working knowledge of
Hadoop ecosystem
(Hadoop, Hive, Pig, Oozie, Hbase, Flume, sqoop) using both automated tool sets as well as manual processes.
Support and maintain
Hadoop (HDP) clusters
for batch processing, analytics, and regulatory reporting.
Perform Hadoop cluster lifecycle management: provisioning, scaling, patching, and decommissioning nodes.
Ensure 24x7 availabilit
y and resilience of production systems supporting payment flows.
Manage and optimize
Apache Kafka
for high-throughput, real-time payment event streaming.
Ensure data consistency and fault tolerance across streaming pipelines.
Support
Apache NiFi
for ingestion pipelines from upstream payment systems and external partners.
Work with
AWS EMR
for scalable processing of transaction data and reconciliation workloads.
Administer HDFS
, ensuring optimal replication, storage utilization, and fault tolerance.
Monitor and tune MapReduce and YARN workloads
to handle large-scale transaction data efficiently.
Ensure proper configuration and validation of jobs handling payment clearing, settlement, and reporting.
Manage
OpenSearch / Elasticsearch clusters
for transaction search, audit trails, and operational dashboards.
Excellent working knowledge of Elastic Kibana
and making pipelines to capture and search relevant data.
Optimize indexing and query performance for near real-time analytics and monitoring.
Implement Kerberos-based authentication and secure access controls across the Hadoop ecosystem.
Manage user provisioning (Linux + Hadoop stack) ensuring least-privilege access.
Ensure compliance with banking regulations, audit requirements, and data governance policies.
Monitor cluster security, encryption, and network connectivity.
Conduct capacity planning
aligned with transaction growth and peak payment volumes.
Optimize systems for low latency and high throughput, critical for digital payments.
Identify bottlenecks and implement performance tuning strategies across platforms.
Ensure high availability through failover mechanisms,
DR strategies, and proactive monitoring.
Develop and maintain runbooks, SOPs, and architecture documentation.
Define and enforce best practices for cluster operations, deployments, and data pipelines.
Contribute to continuous improvement initiatives and knowledge sharing.
Excellent communication, interpersonal and logical skills
Customer service oriented and a strong team player
Ability to work under pressure and a commitment to solving issues
Required Skills & Experience
• 13-15 years of experience in Big Data / Data Platform Engineering in enterprise environments.
•
Strong hands-on experience with
:
• Hadoop ecosystem (HDFS, YARN, SPARK, MapReduce, HBase etc)
• Apache Kafka (high-throughput environments)
• OpenSearch / Elasticsearch
• Apache NiFi
• AWS EMR + good knowledge of AWS Cloud.
• Strong expertise in Linux system administration and scripting (Shell/Python)
• Experience with Kerberos, data security, and access governance
• Proven experience in handling high-volume, low-latency systems (preferably payments/trading)
Work Schedule
v Work schedule is mainly focused to support Asia and EMEA (Paris) time zone; however, may have to support during non-offce hours/weekends/public holidays for critical incidents or escalation as per the assigned on-call support requirements;
v Shift schedule is followed;
Work Hours:
9:00 AM to 6:00PM(Sing Shift) or 2PM to 11PM (Paris Shift).
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