Pune/Pimpri-Chinchwad Area
I build the infrastructure and pipelines that keep production systems fast, reliable, and observable. With 3.5+ years in Site Reliability and DevOps roles at Barclays, I've designed CI/CD pipelines processing 50,000+ daily records, automated self-healing workflows that resolve 10,000+ monthly incidents with 99.9% accuracy, and engineered observability frameworks that cut incident response latency by 70%. What I bring to a DevOps team: CI/CD pipeline design — Jenkins, GitHub Actions, Git, Bitbucket Cloud infrastructure — Microsoft Azure, AWS (S3, Lambda, Glue, Redshift) Containers & orchestration — Docker, Kubernetes IaC & automation — Ansible, Python, Bash scripting Observability & SRE — real-time monitoring, alerting, incident response, RCA I'm driven by SRE principles: reduce toil, improve reliability, and let teams ship with confidence. Most recently I cut system response time from 45s → 8s (82% improvement) and eliminated 95% of error-driven incidents through automated monitoring pipelines. Open to DevOps Engineer, SRE, Platform Engineer, and Cloud Infrastructure roles. Let's connect.
- Designed and deployed CI/CD pipeline workflows using Jenkins and Git, processing 50,000+ daily records with retry logic, structured logging, and automated alerting — achieving 99.5% pipeline accuracy and eliminating 95% of error-driven incidents. - Built real-time observability dashboards (Power BI + DirectQuery) monitoring 12 system health KPIs, cutting incident response latency by 70% and enabling proactive alerting across production systems. - Engineered Python and Bash self-healing automation that identified and resolved 10,000+ monthly discrepancies with 99.9% accuracy, directly reducing operational toil and improving SLA adherence. - Led performance engineering initiative (indexing, query refactoring, execution plan analysis) reducing system response time from 45s → 8s — an 82% improvement achieved through root cause analysis. - Designed scalable cloud infrastructure (Azure) supporting 200GB+ compliance data, using partitioning and materialized views to achieve 60% improvement in query throughput. - Implemented CDC and incremental processing patterns replacing full-refresh cycles, saving 28 compute hours per week and significantly improving system availability. - Drove incident response and RCA across 8+ platform services, collaborating with dev, security, and IT teams on Docker-based containerized deployments and Kubernetes workload orchestration.
- Automated end-to-end pipeline workflows using Python and Bash, consolidating data from 5 disparate systems and processing 15,000+ daily records at 99.8% reliability — eliminating manual operational toil. - Reduced downstream pipeline failure rates by 35% by building scripted validation and transformation workflows enforcing data integrity rules across systems. - Automated 12 recurring operational reporting workflows via scheduled orchestration, replacing 20 hours of weekly manual effort with 100% on-time delivery — a direct application of SRE toil-reduction principles. - Managed version control and pipeline triggers with Git and Bitbucket; explored GitHub Actions to drive adoption of modern CI/CD practices.
- Built real-time operational monitoring dashboards across 50,000+ records, compressing reporting cycles from weekly to daily and enabling faster incident triage. - Developed Python-based automated health-check and anomaly detection framework running 500+ daily validations at 98% accuracy, with auto-flagging for engineer review. - Designed automated audit trail systems using SQL triggers and stored procedures, capturing 100% of system change history and cutting audit preparation time by 60%. - Tuned analytical workloads through query optimization and schema redesign, reducing turnaround time from 2 hours → 15 minutes.