Technical Leadership for Scalable Product Delivery

Technology Consultant

Engineering that creates business value.

I help companies build scalable digital products by bringing together software architecture, product strategy and engineering leadership.

Request a consultation My working approach

Who I work with

  • Startups
  • Scale-ups
  • SMEs
  • Enterprise

Consulting services

Trustedtechnologypartner.

Software investments should do more than keep systems running. They should speed up your processes, reduce operating costs and prepare your company to grow.

Technology for growth

Your technology investment should grow your business.

The right architecture accelerates delivery, reduces operating costs and prepares your company for future growth. I design and deliver that transformation.

  1. 01

    Business goals

    I make sure your software investment supports company goals and measurable outcomes.

    • Faster delivery
    • Lower costs
    • Measurable value
  2. 02

    Right architecture

    I design systems that solve today's needs without limiting tomorrow's growth.

    • Less technical debt
    • Ready for change
    • Scalable systems
  3. 03

    Reliable delivery

    I help new features reach customers safely and predictably.

    • Safer releases
    • Faster development
    • Operational continuity
  4. 04

    Sustainable growth

    I turn technology into long-term efficiency and competitive advantage.

    • Controlled costs
    • Resilient operations
    • Ready to scale

Measured by impact numbers that reflect our growth and trust.

Help when you need it!

Where technology holds you back

Turn your technology investment into a competitive advantage.

Are your systems limiting growth?

Legacy systems and technical debt become an adaptable, sustainable architecture.

Explore the approach

Is delivery slowing down?

Your teams gain the practices needed to deliver faster, predictably and reliably.

Explore the approach

Is technology investment falling short?

Technical decisions align with growth, cost and operational goals, turning investment into measurable business outcomes.

Explore the approach

11+ years · Cloud · AI · Marketplaces · Distributed systems

The right fit

I work with you when the problem is the right one.

The best projects happen when technical goals and business goals move in the same direction. That is why I choose every engagement with care.

Where I create the most value

  • SaaS companies preparing to scale their product
  • Marketplace and e-commerce platforms
  • Teams modernising legacy systems
  • Companies integrating AI into business processes
  • Engineering teams building cloud and distributed systems

It may not be the right fit

  • Companies that make price the only decision criterion
  • Teams that put short-term fixes ahead of long-term architecture
  • Organisations that continually postpone technical debt
  • Teams that change priorities without establishing delivery discipline
  • Companies seeking code delivery without product ownership
Request a technical assessment

Working principles

Stop technology from slowing your company down.

Every technical decision should produce faster delivery, lower risk and sustainable growth.

  1. Reach production faster

    Small changes should not create major release risk.

    New features reach customers sooner.

  2. Bring technical debt under control

    Complexity becomes visible and priorities follow business impact.

    Technical risk no longer blocks growth.

  3. Help teams move independently

    Clear system boundaries reduce how often teams wait on one another.

    Delivery accelerates and dependencies decrease.

  4. Make delivery predictable

    Automation, measurement and observability reduce operational risk.

    Safer releases and more resilient operations.

Work built together

I turn distinct business models into reliable, scalable digital products.

Each engagement is a case study in bringing business context, technical decisions, and delivery discipline into one system.

Kayra Export pazaryeri platformu için geliştirilen mikroservis mimarisiKayra Export: Pazaryeri & E-Ticaret Platformu (CTO)ABC Logistics gerçek zamanlı GIS takip platformu arayüzüABC Logistics: Gerçek Zamanlı GIS Takip PlatformuBare Crypto NFT marketplace DApp logosuBare Crypto: NFT Marketplace Minting DAppRapid Render 3D ürün konfigüratörü için gerçek zamanlı renderingRapid Render: Gerçek Zamanlı 3D Ürün KonfigüratörüDunelm için LiDAR destekli AR view-in-room deneyimiDunelm: LiDAR Destekli AR "View-in-Room" DeneyimiMulcol VR firestop montaj simülasyonuMulcol: VR Firestop Montaj SimülasyonuMulcol için marker tabanlı AR montaj kataloğuMulcol: AR Montaj Kataloğu (Marker-Based)

Featured engagement

Kayra Export: Pazaryeri & E-Ticaret Platformu (CTO)

CTO olarak e-ticaret ve pazaryeri dönüşümünü yönettim; .NET mikroservis + CQRS mimarisi kurup AWS üzerinde ölçekledim. Ödeme/entegrasyon ve yapay zeka destekli modüllerle çok kanallı ticaret altyapısını ürünleştirdim.

See what I delivered in this case study

Perspectives

Technology, growth and better decisions.

Explore more
Architecture · PLAYBOOK

Career perspective: fintech companies aren't looking for Stripe SDK skills — they want failure thinking, reconciliation, idempotency, and evidence-driven…

By Faru
Read more

Chapter trail

Distributed Payment Engine

Part 22 / 22

  1. Part 1 Why Payment Systems Are Distributed Systems
  2. Part 2 Payment State Machine Design: Checkout vs. Payment Lifecycles
  3. Part 3 Why Payment Capture Is Easy but Finalization Is Hard
  4. Part 4 Immutable Payment Snapshot Design: Freezing the Cart at Intent Time
  5. Part 5 Idempotency Beyond API (Application Programming Interface) Requests: A Layered Defense
  6. Part 6 Webhook Reliability in Payment Systems
  7. Part 7 The Outbox/Inbox Pattern in Payment Systems
  8. Part 8 Payment Evidence vs. Payment State
  9. Part 9 Provider Abstraction Without Leaking SDKs
  10. Part 10 Semantic Events Over Raw Provider Payloads
  11. Part 11 Payment Failure Taxonomy
  12. Part 12 Retry Algorithms for Payment Workers
  13. Part 13 DB-Backed Jobs With Leases
  14. Part 14 Building a Payment Reconciliation Worker
  15. Part 15 Healing Paid-But-Unordered Payments
  16. Part 16 Why Eventual Consistency Beats Distributed Transactions
  17. Part 17 Optimistic Concurrency Under Webhooks
  18. Part 18 Payment Observability and Correlation
  19. Part 19 Payment Recovery Pipeline and Runbooks
  20. Part 20 Effectively-Once Processing in Payments
  21. Part 21 Designing a Production Payment Engine
  22. Part 22 What Fintech Companies Actually Hire For
Architecture · PLAYBOOK

The synthesis of a 22-part series: an architectural checklist for a production payment engine built around a checkout orchestrator and provider gateway.

By Faru
Read more

Chapter trail

Distributed Payment Engine

Part 21 / 22

  1. Part 1 Why Payment Systems Are Distributed Systems
  2. Part 2 Payment State Machine Design: Checkout vs. Payment Lifecycles
  3. Part 3 Why Payment Capture Is Easy but Finalization Is Hard
  4. Part 4 Immutable Payment Snapshot Design: Freezing the Cart at Intent Time
  5. Part 5 Idempotency Beyond API (Application Programming Interface) Requests: A Layered Defense
  6. Part 6 Webhook Reliability in Payment Systems
  7. Part 7 The Outbox/Inbox Pattern in Payment Systems
  8. Part 8 Payment Evidence vs. Payment State
  9. Part 9 Provider Abstraction Without Leaking SDKs
  10. Part 10 Semantic Events Over Raw Provider Payloads
  11. Part 11 Payment Failure Taxonomy
  12. Part 12 Retry Algorithms for Payment Workers
  13. Part 13 DB-Backed Jobs With Leases
  14. Part 14 Building a Payment Reconciliation Worker
  15. Part 15 Healing Paid-But-Unordered Payments
  16. Part 16 Why Eventual Consistency Beats Distributed Transactions
  17. Part 17 Optimistic Concurrency Under Webhooks
  18. Part 18 Payment Observability and Correlation
  19. Part 19 Payment Recovery Pipeline and Runbooks
  20. Part 20 Effectively-Once Processing in Payments
  21. Part 21 Designing a Production Payment Engine
  22. Part 22 What Fintech Companies Actually Hire For
Architecture · PLAYBOOK

Exactly-once messaging is a lie. How defense in depth — idempotency, dedup, outbox, and reconciliation — produces an effectively-once business outcome.

By Faru
Read more

Chapter trail

Distributed Payment Engine

Part 20 / 22

  1. Part 1 Why Payment Systems Are Distributed Systems
  2. Part 2 Payment State Machine Design: Checkout vs. Payment Lifecycles
  3. Part 3 Why Payment Capture Is Easy but Finalization Is Hard
  4. Part 4 Immutable Payment Snapshot Design: Freezing the Cart at Intent Time
  5. Part 5 Idempotency Beyond API (Application Programming Interface) Requests: A Layered Defense
  6. Part 6 Webhook Reliability in Payment Systems
  7. Part 7 The Outbox/Inbox Pattern in Payment Systems
  8. Part 8 Payment Evidence vs. Payment State
  9. Part 9 Provider Abstraction Without Leaking SDKs
  10. Part 10 Semantic Events Over Raw Provider Payloads
  11. Part 11 Payment Failure Taxonomy
  12. Part 12 Retry Algorithms for Payment Workers
  13. Part 13 DB-Backed Jobs With Leases
  14. Part 14 Building a Payment Reconciliation Worker
  15. Part 15 Healing Paid-But-Unordered Payments
  16. Part 16 Why Eventual Consistency Beats Distributed Transactions
  17. Part 17 Optimistic Concurrency Under Webhooks
  18. Part 18 Payment Observability and Correlation
  19. Part 19 Payment Recovery Pipeline and Runbooks
  20. Part 20 Effectively-Once Processing in Payments
  21. Part 21 Designing a Production Payment Engine
  22. Part 22 What Fintech Companies Actually Hire For
Architecture · PLAYBOOK

Automation first: the reconciliation worker and recovery pipeline. When uniqueness walls block replay, evidence-driven human runbooks take over.

By Faru
Read more

Chapter trail

Distributed Payment Engine

Part 19 / 22

  1. Part 1 Why Payment Systems Are Distributed Systems
  2. Part 2 Payment State Machine Design: Checkout vs. Payment Lifecycles
  3. Part 3 Why Payment Capture Is Easy but Finalization Is Hard
  4. Part 4 Immutable Payment Snapshot Design: Freezing the Cart at Intent Time
  5. Part 5 Idempotency Beyond API (Application Programming Interface) Requests: A Layered Defense
  6. Part 6 Webhook Reliability in Payment Systems
  7. Part 7 The Outbox/Inbox Pattern in Payment Systems
  8. Part 8 Payment Evidence vs. Payment State
  9. Part 9 Provider Abstraction Without Leaking SDKs
  10. Part 10 Semantic Events Over Raw Provider Payloads
  11. Part 11 Payment Failure Taxonomy
  12. Part 12 Retry Algorithms for Payment Workers
  13. Part 13 DB-Backed Jobs With Leases
  14. Part 14 Building a Payment Reconciliation Worker
  15. Part 15 Healing Paid-But-Unordered Payments
  16. Part 16 Why Eventual Consistency Beats Distributed Transactions
  17. Part 17 Optimistic Concurrency Under Webhooks
  18. Part 18 Payment Observability and Correlation
  19. Part 19 Payment Recovery Pipeline and Runbooks
  20. Part 20 Effectively-Once Processing in Payments
  21. Part 21 Designing a Production Payment Engine
  22. Part 22 What Fintech Companies Actually Hire For
Architecture · PLAYBOOK

How to correlate every log, metric, and trace by payment id — and why a step event log plus deferred finalize metrics save operations.

By Faru
Read more

Chapter trail

Distributed Payment Engine

Part 18 / 22

  1. Part 1 Why Payment Systems Are Distributed Systems
  2. Part 2 Payment State Machine Design: Checkout vs. Payment Lifecycles
  3. Part 3 Why Payment Capture Is Easy but Finalization Is Hard
  4. Part 4 Immutable Payment Snapshot Design: Freezing the Cart at Intent Time
  5. Part 5 Idempotency Beyond API (Application Programming Interface) Requests: A Layered Defense
  6. Part 6 Webhook Reliability in Payment Systems
  7. Part 7 The Outbox/Inbox Pattern in Payment Systems
  8. Part 8 Payment Evidence vs. Payment State
  9. Part 9 Provider Abstraction Without Leaking SDKs
  10. Part 10 Semantic Events Over Raw Provider Payloads
  11. Part 11 Payment Failure Taxonomy
  12. Part 12 Retry Algorithms for Payment Workers
  13. Part 13 DB-Backed Jobs With Leases
  14. Part 14 Building a Payment Reconciliation Worker
  15. Part 15 Healing Paid-But-Unordered Payments
  16. Part 16 Why Eventual Consistency Beats Distributed Transactions
  17. Part 17 Optimistic Concurrency Under Webhooks
  18. Part 18 Payment Observability and Correlation
  19. Part 19 Payment Recovery Pipeline and Runbooks
  20. Part 20 Effectively-Once Processing in Payments
  21. Part 21 Designing a Production Payment Engine
  22. Part 22 What Fintech Companies Actually Hire For
Architecture · PLAYBOOK

When a webhook and a synchronous response touch the same payment at once, how do a version token and a lease resolve the race — and why can a stale read…

By Faru
Read more

Chapter trail

Distributed Payment Engine

Part 17 / 22

  1. Part 1 Why Payment Systems Are Distributed Systems
  2. Part 2 Payment State Machine Design: Checkout vs. Payment Lifecycles
  3. Part 3 Why Payment Capture Is Easy but Finalization Is Hard
  4. Part 4 Immutable Payment Snapshot Design: Freezing the Cart at Intent Time
  5. Part 5 Idempotency Beyond API (Application Programming Interface) Requests: A Layered Defense
  6. Part 6 Webhook Reliability in Payment Systems
  7. Part 7 The Outbox/Inbox Pattern in Payment Systems
  8. Part 8 Payment Evidence vs. Payment State
  9. Part 9 Provider Abstraction Without Leaking SDKs
  10. Part 10 Semantic Events Over Raw Provider Payloads
  11. Part 11 Payment Failure Taxonomy
  12. Part 12 Retry Algorithms for Payment Workers
  13. Part 13 DB-Backed Jobs With Leases
  14. Part 14 Building a Payment Reconciliation Worker
  15. Part 15 Healing Paid-But-Unordered Payments
  16. Part 16 Why Eventual Consistency Beats Distributed Transactions
  17. Part 17 Optimistic Concurrency Under Webhooks
  18. Part 18 Payment Observability and Correlation
  19. Part 19 Payment Recovery Pipeline and Runbooks
  20. Part 20 Effectively-Once Processing in Payments
  21. Part 21 Designing a Production Payment Engine
  22. Part 22 What Fintech Companies Actually Hire For
Explore more →

Before you decide

What you should know before deciding.

Do you need to rewrite the entire system?

Usually not. I first identify the areas creating the greatest business risk and cost, then modernise the system incrementally and with controlled risk.

How soon will you see the first result?

Timing depends on the scope of the problem. I divide the work into small, measurable steps and clarify both quick wins and the long-term roadmap first.

Will you need to replace your team or change how it works?

Usually not. The goal is not to replace the team, but to preserve its knowledge while strengthening decision-making, development and delivery.

How do you measure whether the investment creates value?

I clarify success measures with you at the start, using indicators relevant to the problem such as delivery time, defect rate, operating cost and team wait time.

What happens in the first technical conversation?

I discuss your current system, team and business goals, clarify the priority risks and recommend the most useful next step for you.