Cloud and Data Site Reliability Engineer

Packaged/SaaS Application Engineer

Project Role : Packaged/SaaS Application Engineer
Project Role Description : Configure and support packaged or SaaS applications to adapt features, manage releases, and ensure system stability. Use standard tools, APIs, and low-code platforms to align solutions with business needs while preserving compatibility and performance.
Must have skills : DevSecOps
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Job Description – Cloud & Data - Site Reliability Engineer (Corporate Lakehouse)
Purpose of the Role
The Cloud & Data Site Reliability Engineer (SRE) is responsible for designing, automating, and operating highly reliable, scalable, and secure cloud-native data platforms underpinning the Corporate Lakehouse. The role combines advanced cloud engineering, automation, observability, DataOps, and SRE disciplines to ensure the resilience, performance, and governance of mission-critical data systems across AWS.

Key Accountabilities
Platform Reliability & Operations
Ensure the availability, performance, scalability, and resilience of Corporate Lakehouse systems through proactive monitoring, observability engineering, and capacity planning.
Lead incident response, root-cause analysis, post incident reviews, and implement preventative and architectural improvements.
Design and implement self-healing, auto-recovery, and autonomic operations aligned to SRE principles.
Infrastructure-as-Code, Automation & SDLC
Architect, implement, and operate infrastructure using Terraform Enterprise with strong adherence to:
o DRY principles (modular, reusable, parameterised IaC)
o Core Terraform logic and functions (conditionals, loops, for each, count, dynamic, maps, lists, custom functions)
o Enterprise-grade module composition, workspace strategies, state management, and policy-as-code.
Develop and maintain CloudFormation templates for multi account provisioning and enterprise deployment patterns.
Build automated pipelines using GitLab CI/CD, including automated testing, security scanning, code quality enforcement, and environment promotion.
Use Python to develop automation tooling, operational logic, Lambda functions, CI utilities, and platform workflows.
AWS Engineering & Data Platform Ownership
Engineer cloud-native data capabilities for the Corporate Lakehouse using:
o S3 (core data zones, governance, lifecycle, retention, encryption, access patterns)
o AWS Glue, IAM, KMS, Lambda, CloudWatch, Step Functions, EventBridge
o Patterns for orchestration, ingestion, cataloging, metadata management, and resilience.
Ensure platform governance including logging, monitoring, access control, encryption, tagging, data lineage, and compliance.
Optimise storage, compute, orchestration, and pipeline performance for efficiency and cost-effectiveness.
Performance Monitoring, Observability & Optimisation
Build observability into all layers of the platform (metrics, logs, traces, events).
Identify and eliminate performance bottlenecks across compute, storage, and data flows.
Proactively implement controls, guardrails, and auto-remediation to ensure consistent performance.
Collaboration & Technical Integration
Partner with engineering, data, product, and architecture teams to embed reliability, scalability, and performance into software and data lifecycle processes.
Champion DevOps, DataOps, and cloud automation best practices across the Corporate Lakehouse programme.
Contribute reusable patterns, engineering accelerators, documentation, and enterprise standards.

Core Required Technical Skills
Terraform Enterprise – Expert Level
Mastery of Terraform Enterprise, including:
o DRY, modular design and enterprise module libraries
o Advanced use of core Terraform functions and logic
o Workspaces, state management, policy-as-code (e.g., Sentinel), and environment orchestration
o Secure, scalable engineering patterns for multi account architectures
AWS Cloud Engineering
Deep experience with AWS services including:
o S3, IAM, KMS, Lambda, Glue, Step Functions, CloudWatch, EventBridge
Strong capability in designing secure, resilient, and scalable AWS data platform components.
Automation & Software Engineering
Advanced Python skills for automation, functions, tooling, and operational workflows.
Proficient with CloudFormation for enterprise-grade deployment patterns.
Strong experience with GitLab CI/CD, SDLC pipelines, automated testing, artifact management, and secure deployments.
Data Management Expertise
End-to-end understanding of data governance, retention, metadata, lineage, cataloging, access control, and data architecture patterns within AWS.