more Badges 🥇

                 

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AI certificates

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☁️ Google Cloud Badges 🥇

[Verify all Badges : ](https://www.credly.com/users/akashdip2001)

National award from Dilhi, award taken from Indian precedent Pronob Mukherjee 2012 , on Drawing

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⭐ Credly achievements ⭐

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All office Jira Tasks

old collage projects link

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⚙️ Infrastructure & Workflow Automation ⚙️

Scripts, pipelines, and tools built to eliminate operational toil and accelerate CI/CD workflows.

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Automation Task	Architecture & Implementation	Technical & Business Impact	Links & Stack
Post-Deployment Validation Automation Goal: Automate manual QA checklist for the GSE Events Data Product.	Event-Driven Execution: GitHub Actions workflow dynamically triggers a Python health-check script upon deployment completion. Observability Integration: Uses boto3 to query CloudWatch Metrics/Logs for Lambda exceptions and the datadog-api-client to verify zero Datadog monitor failures during the Change Request (CR) window. Automated Reporting: Generates a structured JSON/Excel checklist enforcing strict Yes/No health validations.	Technical Impact: Eliminated manual log-checking toil. Enforced a deterministic, zero-error baseline for environments, and standardized the Dev sign-off process. Business Impact: Accelerated the Change Request pipeline, saving dedicated engineering hours per deployment. Significantly lowered the risk of regressions reaching production.	Jira link View docomentation Repo Python / Boto3, GitHub Actions, AWS CloudWatch, Datadog API, CI/CD Automation

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💲 AWS Cost Anomaly & Architecture Root Cause Analysis 💲

AWS Billing and Cost Management, AWS CloudTrail, AWS CloudWatch

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Automation Task	Architecture & Implementation	Technical & Business Impact	Links & Stack
AWS Cost Anomaly & Architecture RCA Goal: Resolve runaway AWS billing alerts across Lambda, MSK, and EC2 via deep telemetry analysis.	Telemetry Correlation: Cross-referenced CloudWatch Metrics (Invocations, AsyncEventAge) with large-scale CloudTrail logs to isolate infrastructure changes and filter regional noise. Loop Diagnosis: Uncovered a synchronous Lambda-MSK retry loop triggered by an IAM AccessDenied error following a Kafka client library upgrade. Architecture Audit: Traced a permanent cost baseline shift to manual PutProvisionedConcurrencyConfig executions, changing a Lambda from pay-per-request to 24/7 billing.	Technical Impact: Stopped active runaway costs by identifying abandoned EC2 instances and severing the event loop. Proposed architectural safeguards including DLQs and MSK retry limits. Business Impact: Provided management with definitive data distinguishing between temporary incident burn and permanent architectural upgrades, enabling accurate cloud budgeting.	Jira link View documentation Repo AWS CloudTrail, AWS CloudWatch, AWS Lambda, Amazon MSK, FinOps, Python/Pandas

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⚙️ Other Jira Tasks ⚙️

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Task Name & Goal	Architecture & Implementation	Technical & Business Impact	Links & Stack
CI/CD Pipeline RCA & Architecture Goal: Diagnose and unblock CI/CD test failures.	Event-Driven Pipeline: S3 → EventBridge → Lambda container ETL. Root Cause Analysis: Investigated and resolved pytest test-discovery and import-time execution issues. Fixes: Implemented pytest fixtures, proper NaN handling, and improved mocking.	Technical Impact: Fixed deployment blockers and stabilized test execution. Business Impact: Restored pipeline integrity and improved tooling reliability for the engineering team.	View Repo CI/CD, RCA, AWS Lambda, S3, EventBridge, Python, Pytest
M1.0 Non‑PII Feed QA Sign‑Off Goal: Validate data quality for Cabin Crew Schedules.	Pipeline Validation: Tested S3 → EventBridge → Lambda → SNS FIFO → SQS FIFO. Execution: Ran 15 NOT NULL negative validations and checked schemas against the Confluence Data Dictionary. Observability: Tracked DLQ routing via CloudWatch Logs Insights.	Technical Impact: Built a repeatable QA harness using a dedicated SQS FIFO queue. Business Impact: Ensured downstream data reliability and provided documented sign-off evidence.	Jira Repo Published Confluence AWS S3, EventBridge, Lambda, SNS/SQS FIFO, CloudWatch
M1.0 Non-PII API Data Validation Goal: Validate data integrity and PII hashing for Cabin Crew Details.	Pipeline Validation: Tested S3 → EventBridge → MSK (Kafka) → API Gateway. Execution: Built a Python automation script (deepdiff) for 1:1 payload comparison, verifying strict SHA-256 hashing of sensitive fields across 2,500+ records. Observability: Validated AVRO format serialization in raw and curated Kafka topics.	Technical Impact: Automated manual JSON comparison processes, accelerating QA regression testing and eliminating human error. Business Impact: Certified the secure obfuscation of PII data for downstream consumers, meeting strict Pathfinder security requirements.	Jira Repo Published Confluence AWS API Gateway, MSK (Kafka), Python, Postman, Lambda
Datadog Observability & RCA Goal: Diagnose production data pipeline anomalies.	SNS Failures: Correlated Datadog metrics and CloudWatch to find orphaned .fifo subscriptions pinging deleted SQS queues. S3 4xx Errors: Isolated NoSuchKey errors to missing daily data partitions causing concurrent Lambda crashes.	Technical Impact: Created deep-dive operational runbooks for serverless architectures. Business Impact: Reduced alert noise and improved incident response times for production pipelines.	View Repo Datadog, AWS SNS/SQS, AWS S3, CloudWatch Logs Insights, RCA
Vulnerability Fix (DevSecOps) Goal: Remediate critical CVEs in containerized Lambdas.	Remediation: Fixed 3 critical CVEs via dependency upgrades (pip, gnupg2). Optimization: Implemented multi-stage Docker builds to restructure Dockerfiles. Validation: Used CloudWatch metrics and ECR image analysis to verify deployments.	Technical Impact: Hardened container security and achieved a 42% image size reduction (607MB → 350MB). Business Impact: Mitigated security risks and reduced AWS Lambda cold-start overhead.	View Repo Docker, GitHub Actions, AWS ECR, AWS Lambda, Security/CVE
Kafka Partition Learnings Goal: Master event streaming system fundamentals.	System Design: Conducted architectural experiments focusing on Kafka partitioning, throughput, and ordering. Infrastructure: Deployed test environments using AWS ECS, EC2, and S3.	Technical Impact: Built strong, hands-on fundamentals for scaling distributed systems. Business Impact: Established core knowledge to support reliable, high-throughput data engineering.	View Repo Kafka, AWS ECS, AWS EC2, AWS S3, Distributed Systems
Post-Deployment Validation Goal: Automate manual QA checklist for deployments.	Event-Driven Execution: GitHub Actions dynamically triggers a Python health-check script. Observability: Uses boto3 for CloudWatch metrics and datadog-api-client to verify zero monitor failures. Reporting: Generates a structured JSON/Excel deployment checklist.	Technical Impact: Eliminated manual log-checking toil and enforced a zero-error baseline. Business Impact: Accelerated the Change Request pipeline and significantly lowered regression risks.	Jira Repo Python/Boto3, GitHub Actions, AWS CloudWatch, Datadog API
Secure Data Feed Migration (GTA) Goal: Transition consumer endpoints from PII to anonymized non-PII data feeds.	IaC Configuration: Authored Terraform updates to securely re-route AWS IAM resource permissions. Deployment Pipeline: Synced configurations systematically across UAT and Production environments. Validation: Validated changes via GitHub Actions CI/CD and managed the formal deployment lifecycle via ServiceNow.	Technical Impact: Maintained seamless data delivery while safely deprecating legacy sensitive data access. Business Impact: Enforced strict enterprise data security standards and successfully navigated the Change Advisory Board (CAB) approval process.	Jira Terraform, AWS IAM, GitHub Actions, ServiceNow
[Onboarding] Secure Data Feed Migration (GTA) Goal: Transition consumer endpoints from PII to anonymized non-PII data feeds.	IaC Configuration: Authored Terraform updates to securely re-route AWS IAM resource permissions. Deployment Pipeline: Synced configurations systematically across UAT and Production environments. Validation: Authored enterprise-standard implementation plans, secured senior engineer validation, and managed the CAB approval lifecycle via ServiceNow.	Technical Impact: Maintained seamless data delivery while safely deprecating legacy sensitive data access. Business Impact: Enforced strict enterprise data security standards and mastered cross-functional enterprise deployment workflows.	Jira Repo Terraform, AWS IAM, GitHub Actions, ServiceNow

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🛡️ Application Security & Code Hardening 🛡️

TCS contest

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Event / Project	Description & Remediations	Link	Tags
TCS CodeSecure2 Hackathon: Django Security Audit	Conducted a comprehensive static code analysis and manual security remediation for a monolithic Django application (Sales & Inventory Management). Successfully identified and patched 27 critical vulnerabilities mapping to the OWASP Top 10. Key Vulnerabilities Remediated: Injection Flaws (SQLi & OS Command): Replaced raw SQL execution (cursor.execute) with secure, parameterized Django ORM queries. Completely removed multiple instances of OS Command Injection by stripping unsafe subprocess.check_output() calls tied to URL parameters. Insecure Deserialization & File Uploads: Prevented Remote Code Execution (RCE) by migrating unsafe pickle.loads() data parsing to secure json.loads(). Patched Arbitrary File Read (SSRF) and Path Traversal flaws in file-handling views. Broken Access Control & IDOR: Eradicated hidden backdoor login routes. Secured API endpoints by removing unauthorized state-changing GET methods and enforcing strict authentication decorators (@login_required, LoginRequiredMixin) to prevent Insecure Direct Object References (IDOR) on sensitive invoice downloads. Global Configuration Hardening: Hardened settings.py by disabling DEBUG mode, securing session cookies (HTTPOnly), removing wildcard "null" CORS misconfigurations, enforcing Clickjacking middleware (X_FRAME_OPTIONS), and externalizing hardcoded secret keys.	Repo	use Python / Django Code Review OWASP Top 10 AppSec, Vulnerability Remediation, DevSecOps, SQLi, Command Injection, IDOR, Deserialization, CORS, Security Audit

Repository Visibility Notice.

• Some repositories may currently appear as private or return a 404 error.
• This is intentional. As I am presently part of an organization, I periodically restrict repository visibility to ensure full compliance with corporate policies, confidentiality standards, and professional ethics.
• If you are a recruiter or engineer interested in reviewing my work, feel free to contact me directly, and I’ll be happy to discuss my experience and learning approach.

my interest

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These repositories reflect my learning journey and engineering mindset, and they continue to evolve as I gain deeper exposure to DevOps, Cloud, Security, and Distributed Systems.

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