Javier Sánchez Lancha is a Backend Software Engineer focused on Java and Spring across production platforms, with SAP Commerce Cloud as a strong specialization.
My work combines REST APIs, SQL, integrations, incidents, and close collaboration with QA, business teams, and clients.
4+ years experience
Java Backend
Production support
SAP Commerce expert
Technologies used: Java, Spring, SAP Commerce, Docker, GitHub, Postman, Jira, Codex, Notion.
Experience
From SAP and B2B foundations to a stronger Java and Spring backend profile shaped by context, integrations, and product decisions.
Current experience
2024 - PresentDirect experience in production support, functional evolution, and incident resolution with real business impact.
Recent experience
2024Strengthened backend judgment around business-sensitive commerce flows and around how to extend SAP Commerce Cloud carefully.
Professional foundation
2023 - 2024Built a strong professional foundation in Java backend, collaboration with functional teams, and work on a real SAP Commerce Cloud platform.
Early experience
2022 - 2023Built a strong technical foundation in business software, SAP/ABAP development, and early SAP Commerce backend work.
Featured work
Three concrete cases showing the problem, the technical decision, and the impact of each intervention.
Stratesys · CaixaBank Tech · 2025
ProductionKeeping a business-critical platform stable while shipping backend changes and investigating production issues with clear functional context.
Minsait · Claro Perú · 2024
ProductionImplementing changes in checkout, pricing, stock, and integrations without degrading baseline platform behavior.
AI applied to technical work
Since my current stage at Stratesys, I use generative AI to accelerate technical analysis, debugging, and change preparation while keeping technical decisions, local testing, and pre-QA validation under my own responsibility.
View real casesAI as accelerator
I review logs, traces, code, and business context.
AI detects patterns and proposes hypotheses.
I get a summary, likely causes, and options.
I validate and decide
I validate hypotheses with tests and code review.
I review standard behavior, impact, and functional criteria.
I make the final decision and deploy the solution.
It speeds up analysis, information lookup, and option generation to save time on cognitive and repetitive tasks.
I decide, validate, test, and own the technical outcome and the production impact.
AI does not replace experience or technical judgment. Its greatest value is getting to useful context sooner, reducing repetitive work, and leaving more time to solve complex problems and make better engineering decisions.
Day-to-day impact
Complex changes
~0%
less time
On analysis and initial preparation of complex changes when the context is comparable.
1-2 day tasks
< 0
day
When the context is bounded and validation still remains mine.
Incidents
minutes
first hypothesis
To locate likely causes before debugging and validating.
Estimates based on my own metrics from comparable tasks completed before and after introducing generative AI into my workflow. They are not universal benchmarks, but observed impact in my professional context.
About
My profile has grown around real systems: production platforms, integrations, incidents, business logic, and collaboration with technical and functional teams.
I understand flows, affected data, and risks before implementing.
I analyze symptoms, logs, and hypotheses before defining a solution.
I translate technical impact into clear next steps for QA, clients, and business teams.
I use it as an accelerator, never as a substitute for technical judgment.
Let’s talk about Java, Spring, integrations, and how I can contribute from the first technical context.
