ATS CONSULTING SERVICES PH INC.
Senior Data Platform Reliability Engineer
Operates, maintains, and continuously improves the company's data platforms on Kubernetes (on-prem and AWS/GCP EKS/GKE), handling GitOps deployments, monitoring, incident response on 24x7 rotations, and troubleshooting user issues while mentoring junior engineers. Core stack includes Kubernetes, Linux, and ETL/ELT tooling like Spark, Airflow, and Python/SQL.
- Compensation
- Not disclosed
- Location
- Mandaluyong, Philippines
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3+ years of solid experience supporting production data workloads/platforms (Spark/Airflow/Jupyter)
- 5+ years of hands-on experience on ETL/ELT pipeline development & data transformations (Python/Java & SQL)
- Practical proficiency in Kubernetes environments including cloud-provider managed flavors (AWS-EKS/GCP-GKE)
- Comprehensive knowledge of Linux environments, microservice architectures and service communication patterns
- Strong troubleshooting fundamentals such as application crashes, resource contentions, service latency, and scaling behaviour
- Well-rounded competency in analysing logs, metrics, monitoring systems, and service KPIs
- Hands-on experience with automation or scripting (Bash, Python)
Nice to have
- Exposure to other Data/AI platforms such as Flink, Trino, Druid, and Ray
- Kubernetes or Data certifications (CKAD, AWS Certified Data Engineer)
Full description
About the role
As a Senior Data Platform Reliability Engineer, you will be responsible for operating, maintaining, and continuously improving the company's data platforms running on Kubernetes (on-premises and/or on AWS/GCP) - similar to the DoEKS (Data on EKS) / AIoEKS (AI on EKS) deployment frameworks.
Key responsibilities
- Deploy new releases and configuration changes through GitOps/DevOps
- Monitor platform and service health using logs, metrics, and observability tools
- Participate in incident response, root cause analysis, and 24x7 operational rotations
- Improve platform observability, operational tooling/automations, self-service capabilities, and reliability practices to reduce recurring issues
- Investigate & troubleshoot user concerns by either correlating them to system-related issues, breaking integrations, and/or user-specific errors/misconfigurations up to recommending/executing resolutions
- Provide technical mentorship to junior engineers
- Advocate for platform standards, security best practices, and operational excellence
About you
- 3+ years of solid experience supporting production data workloads/platforms (Spark/Airflow/Jupyter)
- 5+ years of hands-on experience on ETL/ELT pipeline development & data transformations (Python/Java & SQL)
- Practical proficiency in Kubernetes environments including Cloud-provider managed Kubernetes flavors (AWS-EKS/GCP-GKE)
- Comprehensive knowledge on Linux environments, microservice architectures and service communication patterns
- Strong troubleshooting fundamentals such as application crashes, resource contentions, service latency, and scaling behaviour
- Well-rounded competency in analysing logs, metrics, monitoring systems, and service KPIs
- Exposure to other Data/AI platforms such as Flink, Trino, Druid, and Ray
- Hands-on experience with automation or scripting (Bash, Python)
- Kubernetes or Data certifications (CKAD, AWS Certified Data Engineer)