Caching Strategy
CDN, Redis, and in-memory caching reduce repeated compute and database reads.
Solves: Hot paths overloaded the database and slowed API responses.
Impact: Lower p95 latency and fewer expensive downstream calls.
Ali Burak Başaran, also known as Burak Başaran, is a software architect focused on microservices, AI development, cloud architecture, and scalable software development.
Senior Software Architect · Head of Engineering
Architecting Scalable Systems & Leading High-Impact Engineering Teams
15+ years building AI-enabled, cloud-native, microservice-based platforms and guiding engineering organizations from complexity to measurable business outcomes.
0+
years experience
0+
engineers led
1.0%
downtime reduction
1.0%
faster deployments
Enterprise Architecture
Software Architect & Enterprise Architect
Designing resilient platforms where domain models, integration patterns, and operational quality work as one system.
Microservices, DDD, and event-driven systems
High availability and observability-first architecture
Architecture governance aligned with business velocity
1.0%
performance improvement
1.0%
downtime reduction
Clients
Web · Mobile · Partner APIs
API Gateway
Routing · Auth · Rate limits
BFF / Edge
Experience orchestration
Cloud Edge
WAF · CDN · Zero trust
Event BusKafka / Service Bus
Identity
Own DB
Catalog
Own DB
Order
Own DB
Payment
Own DB
Notification
Own DB
Analytics
Own DB
Observability
Logs · Metrics · Traces
Data Platform
Streams · Lakehouse · BI
CI/CD Guardrails
Quality gates · IaC
High Traffic APIs
Scaling Systems for High Traffic & Low Latency APIs
From monolith systems to distributed, high-performance architectures.
Scaling Journey
1
Initial System
Monolithic APISingle databaseNo cachingLimited concurrency (~10k users/day)
2
Bottlenecks
High DB loadSlow API responses (500-900ms)Increased error ratesBlocking synchronous flows
3
Architecture Improvements
Introduced Redis caching layerCDN caching at edgeDatabase read replicasAsync processing with queuesRate limiting & throttling
4
Scaled System
High throughput (10x traffic handling)Low latency (<150ms p95)Improved reliabilityHorizontal scaling enabled
Request Flow Architecture
Client
CDN/Edge Cache
cacheAPI Gateway
Services
Redis
cacheDB Replicas
readsPrimary DB
writesMetrics Impact
Latency
120ms
800ms → 120ms
Throughput
10x
1x → 10x
Error Rate
0.8%
6.5% → 0.8%
Uptime
99.9%
97.5% → 99.9%
Engineering Trade-offs
Caching vs consistency
Redis improves latency but introduces cache invalidation complexity.
Microservices vs operations
Microservices increase scalability but raise operational overhead.
Async throughput vs debugging
Async systems improve throughput but increase debugging complexity.
High Traffic Design Patterns
Load Balancing
Horizontal scaling distributes traffic across healthy service instances.
Solves: Single-node pressure created unpredictable response times.
Impact: Higher concurrency and more stable throughput under spikes.
Database Scaling
Read replicas and shard-aware boundaries move reads away from the primary database.
Solves: Read-heavy workloads competed with critical writes.
Impact: Improved database headroom and lower query contention.
Async Processing
Queues and event-driven flows move non-critical work out of the request path.
Solves: Synchronous side effects blocked user-facing API responses.
Impact: Faster APIs and more resilient background processing.
Rate Limiting
Throttling protects services from abusive clients and sudden traffic bursts.
Solves: Unbounded traffic could exhaust shared resources.
Impact: Predictable service protection and fair resource usage.
Resilience
Circuit breakers, retries, and timeouts contain downstream failure modes.
Solves: Dependency failures cascaded across services.
Impact: Better uptime and faster recovery during partial outages.
Decision Framework
Architectural Decisions & Trade-offs
How I think, evaluate, and decide in complex system design problems.
Problem Context
A growing product surface caused deployment coupling, unclear ownership, and slower delivery across teams.
Options Considered
Keep modular monolith
Full microservices split
Incremental bounded-context extraction
Trade-offs
Modular monolith keeps operations simple but limits independent scaling.
Full split increases autonomy but creates high migration and platform cost.
Incremental extraction balances risk, ownership, and delivery continuity.
Final Decision
Extracted services incrementally around stable bounded contexts while keeping shared platform guardrails.
Outcome
Improved team autonomy without a big-bang rewrite and reduced release coordination overhead.
Trade-off Visualization
PerformanceComplexity
ScalabilityMaintainability
CostSpeed
Engineering Principles I Follow
1Optimize for flow, not utilization.
2Prefer simple systems over clever systems.
3Measure before optimizing.
4Design for failure, not success.
Engineering Leadership
Engineering Manager & Team Leader
Building teams that can make strong technical decisions, ship predictably, and grow through mentoring culture.
Leading 20+ engineers across product and platform teams
Mentoring systems that scale senior judgment
Agile delivery with accountable ownership
1.0%
milestone delivery predictability
1.0%
faster deployments
Engineering Leadership
Platform
Product
Data
QA
DevOps
UX
Engineering Excellence
Driving Engineering Excellence with DORA Metrics
Improving delivery performance by making speed, stability, and recovery visible to every engineering team.
Deployment Frequency
How often teams successfully release value to production.
What I did
Introduced trunk-based delivery and automated release pipelines
Reduced manual approvals with quality gates and observability checks
Impact
Moved teams toward weekly and daily release capability with 60% faster deployments.
Lead Time for Changes
The time from code commit to production-ready business value.
What I did
Streamlined CI/CD workflows and removed delivery bottlenecks
Split large changes into smaller deployable increments
Impact
Reduced delivery cycle time by up to 70% across critical product flows.
Change Failure Rate
The percentage of production changes that cause incidents or rollbacks.
What I did
Added automated test coverage, release guardrails, and progressive rollout patterns
Strengthened ownership with post-release metrics and incident learning loops
Impact
Improved delivery confidence while reducing production risk and rework.
Mean Time to Recovery
How quickly teams restore service after a production incident.
What I did
Designed observability dashboards, alerting flows, and incident response routines
Improved service boundaries to isolate failures faster
Impact
Reduced downtime by 80% through faster detection, response, and recovery.
Developer Experience
Improving Developer Experience at Scale
Creating engineering flow by reducing friction, accelerating feedback loops, and making developer happiness a measurable operating concern.
Key Metrics Dashboard
AfterCI/CD Pipeline Duration
6min
18min → 6min
PR Review Time
6h
24h → 6h
Onboarding Time
1.5days
5days → 1.5days
Deployment Friction
2/10
8/10 → 2/10
Build Success Rate
96%
82% → 96%
Developer Journey
Code
Standardized dev shell
PR
Small reviewable slices
Review
6h SLA
Merge
Owned services
Deploy
Automated guardrails
Improvement System
CI pipeline parallelization
Problem: Slow serial jobs created long feedback cycles and release hesitation.
Action: Split build, test, security, and packaging stages into parallel quality gates.
Result: Pipeline duration improved from 18 minutes to 6 minutes.
Test optimization
Problem: Unclear test boundaries made suites slow, flaky, and hard to trust.
Action: Separated unit, integration, and contract tests with smarter execution rules.
Result: Faster feedback with higher confidence before merge.
Local environment improvements
Problem: New engineers lost days configuring dependencies and sample data.
Action: Standardized local setup with scripts, containers, fixtures, and runbooks.
Result: Onboarding dropped from 5 days to nearly 1.5 days.
PR size reduction strategy
Problem: Large pull requests delayed reviews and increased merge risk.
Action: Introduced smaller change patterns, ownership rules, and review SLAs.
Result: Review time improved from 24 hours to 6 hours.
Code ownership and tooling standards
Problem: Ambiguous ownership caused slow decisions and inconsistent delivery quality.
Action: Defined ownership maps, templates, linters, and shared engineering standards.
Result: Build success rate increased to 96% while deployment friction dropped sharply.
AI & Cloud
AI & Cloud Solutions Engineer
Designing enterprise AI and cloud platforms that connect secure data foundations, production-grade LLM workflows, and measurable business outcomes.
Enterprise AI strategy across Azure AI, Vertex AI, Microsoft Fabric, and AWS-native services
RAG platforms, evaluation pipelines, prompt/version governance, and fine-tuning when justified
Data quality, vector search, guardrails, observability, model risk controls, and secure AI operations
1.0%
conversion increase
Data ingestion → retrieval → reasoning → business action
Companies & Impact
Executive engineering across product, logistics, and fintech
A career shaped by scale, reliability, modernization, and business-aligned engineering.
RoofStacks & GoArt WorldsBorusan LojistikFarmazonOdeon
RoofStacks & GoArt Worlds
Head of Engineering / Software Architect
Led engineering teams across immersive products, AI systems, and scalable platform foundations.
Borusan Lojistik
Senior Architect
Modernized logistics systems with cloud-native, event-driven architecture and reliability improvements.
Farmazon
Engineering Leader
Scaled marketplace technology and delivery systems with strong engineering operating models.
Odeon
Software Architect
Improved enterprise software delivery through domain-focused architecture and platform practices.
Earlier Career
Senior Software Engineer
Built deep technical foundations in .NET, databases, integrations, and high-availability systems.
Professional Journey
A progression from strong engineering to strategic technical leadership
No date-heavy CV. Just the operating arc behind the outcomes.
01APIAICQRSDDDRAG
Technical Range
Deep enough to guide, broad enough to connect systems
A stack shaped by production demands, enterprise constraints, and AI-era product strategy.
Backend (.NET)
C#.NETASP.NET CoreRESTgRPCCQRSSaga PatternClean Architecture
Cloud
AzureAWSGCPKubernetesDockerCI/CDObservability
Databases
SQL ServerPostgreSQLRedisMongoDBElasticsearchCosmos DBOracleMySQLEvent StoresSearch
AI/ML
PythonEnterprise AIAzure AIVertex AIMicrosoft FabricLLMsRAGVector DBsEvaluationAutomation
About
Leadership philosophy built on clarity, leverage, and accountable autonomy
I help organizations translate technical complexity into reliable platforms, faster delivery, and teams that can scale judgment. The goal is not architecture for its own sake; it is a system where product ambition, engineering quality, and operational excellence reinforce each other.
Operating principles
Make architecture measurable.
Build leaders, not dependency chains.
Favor simple systems with strong boundaries.
Contact
Let’s build somethingscalable
Open to strategic engineering leadership, architecture advisory, and AI transformation conversations.