Capacity Engineer

About the client:

Our client is a global technology consulting and digital solutions company that enables enterprises across industries to reimagine business models, accelerate innovation, and maximize growth by harnessing digital technologies.

Role--Capacity Engineer with Data Engineer

Location --Remote 

• Data Engineering Stack: SQL, Python, Spark, Airflow for data processing and orchestration.  
• Monitoring & Observability: Prometheus, Grafana, Datadog.
• Chaos Engineering: Test system resilience under stress.
• Infrastructure as Code: Terraform, Ansible, Harness.

Data Engineer with strong Site Reliability Engineering (SRE) expertise in capacity planning. This role ensures our infrastructure scales efficiently to meet user demand, balancing performance with cost. The engineer will forecast growth, analyze usage trends, and automate resource provisioning to prevent outages, over-provisioning, or under-provisioning. In addition, the role requires building robust data pipelines and analytical models to support forecasting and decision-making.
Key Responsibilities

·       Data Pipeline Development: Design and maintain ETL/ELT pipelines to collect, transform, and store infrastructure usage data.

·       Data Modeling: Build models to analyze system metrics and predict future resource needs.

·       Demand Forecasting: Analyze historical usage patterns to predict CPU, memory, and storage requirements.

·       Load Testing & Scaling: Simulate traffic spikes to identify bottlenecks and ensure systems scale linearly.

·       Cost Efficiency: Optimize resource allocation to avoid unnecessary costs while maintaining service availability.

·       Automation: Use Infrastructure as Code (IaC) tools like Terraform to automate scaling and provisioning.

·       Architecture Review: Collaborate with software teams to flag single points of failure and ensure resilient service design.

Tools & Techniques

·       Monitoring & Observability: Prometheus, Grafana, Datadog.

·       Chaos Engineering: Test system resilience under stress.

·       Infrastructure as Code: Terraform, Ansible, Harness.

·       Data Engineering Stack: SQL, Python, Spark, Airflow for data processing and orchestration.

Qualifications

·       Strong background in data engineering and SRE practices.

·       Hands-on experience with capacity planning, forecasting, and scaling.

·       Proficiency in IaC tools (Terraform, Ansible, Harness).

·       Experience with data pipelines, ETL/ELT frameworks, and big data tools.

·       Familiarity with monitoring/observability platforms (Prometheus, Grafana, Datadog).

·       Knowledge of chaos engineering and resilience testing.

·       Excellent collaboration and communication skills.