• Monitor, support and continuously enhance production pipelines to ensure data availability and data accuracy targets are consistently met.
• Define, design and optimise data modelling strategy within data lake, while establishing design standards, best practices and development methodologies to ensure our data models aligns with user needs and business goals.
• Collaborate with the Data Infrastructure Manager to identify, implement and drive ongoing enhancements, ensuring that the data architecture and infrastructure remain scalable, adhere to industry standards, and continually improve load time and cost efficiency.
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Data Governance & Quality: Implement data quality monitoring frameworks, enforce privacy compliance (PDPA), and maintain data lifecycle policies including retention and archiving.
Cross-Functional Collaboration: Support Business Analytics and Data Science teams by disseminating clean, high-quality datasets and delivering automated downstream reporting extracts.
2 to 5 years of experience in data engineering, custom Python ETL scripting, scalable pipeline orchestration, and cloud infrastructure management.
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Write, optimize and troubleshoot complex T-SQL queries, stored procedures, views, functions and triggers to support data transformation, loading, and validation logic within MS SQL Server, in line with established coding and naming standards.
Orchestrate and schedule complex data workflows, ensuring timely and reliable data delivery.
Implement and manage monitoring solutions to proactively address and prevent performance issues. Monitor daily job statuses and perform troubleshoot and recovery when necessary.
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Performance & Cost Optimisation: Monitor, troubleshoot, and continuously optimise data workflows to improve performance, resilience, and cost efficiency.
Data Storage & Management: Build, manage, and maintain GIGA’s Data Mart, data lake, and related data storage solutions to support department’s reporting, analytics, and advanced use cases.
Data Modelling & Architecture Collaboration: Develop and maintain data models that support analytics, operational reporting, and decision-making tailored for audit and investigation activities. Collaborate closely with Group IT and Group Digital teams on data architecture decisions to support current and future data requirements.
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Maintain and manage the observability of infrastructure environment of Data Engineering, including monitoring, logging, alerting, notification and so on, focusing on the monitor system performance and proactively identify and resolve issues. Optimize system performance and resource utilization.
Work closely with cross-functional teams to understand business needs and deliver data-driven insights.
Provide technical support, data ops environment on-call and training to team members and stakeholders on data operation tools and best practices.
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Must be able to manage database processes and reporting structures
Extract and analyse data from core business systems such as engineering softwares, CRM,ERP, excel, or operational platforms using SQL or other appropriate methods.
Validate data accuracy, completeness, consistency; identify gaps or anomalies
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Work with Service Center to ensure average turn around time (TAT) is within agreed KPI thresholds.
Create a weekly report to send to the Info Systems Project Management team to assist them in reviewing incidents to determine what events have occurred that may be operational losses.
Work with Service Center to ensure average turn around time (TAT) is within agreed KPI thresholds.
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Operate DataPipes (EKS + Airflow) for ingestion orchestration, ensuring high availability and version-controlled configurations via IaC (CloudFormation/CDK).
Maintain & enhance logging, monitoring, observability & DevOps automation via modern tools such as CloudWatch, Splunk, PagerDuty, Slack, ServiceNow, Snowflake observability features.
Own the data ops CI/CD service and its deployment templates.
Onboard new projects and repositories onto the CD automation; own the standard folder structure template and enforce its use. Maintain build and deployment reliability: pipeline runtime, failure rate, and diagnosability of failed builds.
Drive continuous improvement of the pipeline: build caching, container image hygiene, secret handling, environment promotion, and rollback paths.
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You will design and implement the platform's identity, access and security model across AWS IAM, Kubernetes RBAC and service identities, working with the storage access and security teams.
You will build the observability, alerting, capacity planning, and incident tooling for the platform. You will contribute to the SRE practice, which includes SLOs, runbooks, on-call, and post-incident reviews. You will reduce toil and MTTR.
You will own compute cost efficiency: instance and storage strategy, spot and right-sizing, bin-packing, idle reclamation, and cost attribution back to tenants.
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Manage SageMaker environments (Studio, Canvas, Notebooks) for enabling multi-domain ML use cases.
Operate DataPipes (EKS + Airflow) for ingestion orchestration, ensuring high availability and version-controlled configurations via IaC (CloudFormation/CDK).
Maintain & enhance logging, monitoring, observability & DevOps automation via modern tools such as CloudWatch, Splunk, PagerDuty, Slack, ServiceNow, Snowflake observability features.
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Operate DataPipes (EKS + Airflow) for ingestion orchestration, ensuring high availability and version-controlled configurations via IaC (CloudFormation/CDK).
Maintain & enhance logging, monitoring, observability & DevOps automation via modern tools such as CloudWatch, Splunk, PagerDuty, Slack, ServiceNow, Snowflake observability features.
Design and improve clickstream data pipelines—from event ingestion to insight delivery—and contribute to services/tools for understanding user behavior.
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Assess the commercial potential and technical readiness levels of internal R&D projects for potential spin-offs or new business incubation.
Bachelor's degree in Engineering (e.g., Electrical, Mechanical, Chemical, Software, Materials Science, Biomedical) or a relevant scientific discipline from an accredited institution.
Master's or Ph.D. in a relevant engineering or scientific field is highly preferred.
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