Lenny Daume
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SEOlytics GmbH Logo

SEOlytics GmbH

Hamburg

SEO Backend Development with Big Data

Backend Development for Scalable SEO Data (BigData)

November 2013 - October 2014
1 year
Full-time
Product work
Hamburg

Relevance

Why this case matters

This framing makes the decision signal explicit: impact, proof, fit, and AI / delivery relevance for hiring or collaboration.

System impact

Helped build scalable SEO backend systems across Java/Scala, microservices, REST/JMS, Daily Rankings, databases, AWS/Docker, Google Search Console, release management, and team coaching.

AI / delivery relevance

AI-native systems depend on data quality, interfaces, and reliable backends. This case shows that technical foundation.

Big Data BackendJava/ScalaMicroservicesRelease Management

Proof

Multi-DB

Data architecture

AWS/Docker

Platform work

GSC

Data integration

Especially relevant for

  • For teams with data-intensive products where backend, data model, and delivery need to fit together.
  • For organizations that need technical depth and team coaching inside ongoing product development.

Case context

Overview

SEOlytics needed backend systems for large SEO data flows and faster technical adaptation. I worked on microservice architecture, REST/JMS communication, Daily Rankings data, database reengineering, implementation, architecture, testing, and team coaching.

The technical work connected Java/Scala, Docker/AWS, Google Search Console, Search Analytics, and own services with TDD, CI, and release management. The point was not only big-data processing, but a backend that could absorb market changes faster while staying maintainable.

Responsibility

Activities

  • Microservice Architecture: New and further development of the microservice landscape, scalable architecture
  • REST/JMS landscape: Made backend applications and queues more maintainable as a distributed system
  • SEO data models: Connected Daily Rankings, position dailies, and Google Search Console data to scalable processing
  • Database Reengineering: Performance optimization, data model design, Big Data integration
  • Technical Leadership: Team coaching for implementation, architecture, design, testing, documentation
  • Big Data Processing: Java/Scala, Docker/AWS, Google APIs for SEO data processing

Operating mode

Methodology

  • Test-Driven Development: Built-in quality, automated tests, and mocking/stubbing for risky backend changes
  • Continuous Integration: Automated builds, release management, and quality gates as a fast feedback system
  • Visible architecture decisions: Keep data models, microservice boundaries, and performance work inspectable
  • Team enablement: Use coaching, reviews, and documentation so architecture knowledge does not stay with isolated individuals

Technical context

Technology stack

The tools are not the point by themselves. What matters is which system layers had to work together.

9Areas
39Technologies

Backend

6
Java 8ScalaGroovyPlay FrameworkAkkaREST APIs

Frontend

2
JavaScriptJSON/XML

DevOps

6
DockerAWS EC2AWS GlacierCloud InfrastructureGitSVN

Databases & Storage

7
AWS S3MongoDBCassandraNeo4jArangoDBMySQLBig Data Storage

Data & AI

4
NLPGoogle APIs (AdwordsGoogle Search ConsoleSearch Analytics

CI/CD & Delivery Pipelines

3
JenkinsCI/CD PipelineNexus

Tools

7
JUnitTestNGSpockMockitoWebmasterTools)JiraConfluence

Practices

3
Test-Driven DevelopmentProject ManagementDocumentation

Messaging & Event Streaming

1
JMS

Next step

If you want to explore similar leverage for hiring, collaboration, or a concrete transformation, this is the right starting point.

Send a short note about the situation you are trying to assess. I reply personally and will be direct about fit.

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