
Elasticsearch Experts
to power search, analytics and AI-ready data experiences with fast, precise matchingHire experts who design search architectures, build relevance-focused queries and connect Elasticsearch with observability, data pipelines and cloud infrastructure. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts who have recently used Elasticsearch
Max R.
Last position:
Senior AI Engineer and Technical Advisor (Freelancer) at dialpad
Serving as AI technical advisor to Dialpad's Telephony Engineering division while building AI products hands-on. Currently developing MCP-based tooling for AI text and voice agents: agent authoring, integrations and adoption of existing agents, shared authentication and API contracts, and durable workflow state. This includes text and voice tests that evaluate agent behavior, latency and task completion.
In parallel, helping Dialpad's engineers adopt QualityLayer and use Claude Code and Codex effectively. This ongoing work covers context management for large codebases, reusable plan-implement-test-review workflows connected to CI/CD, playbooks, hands-on workshops, labs and pair programming.
Technologies: AI Agent Products (Text & Voice Agents, MCP, LLMs, Python, TypeScript), Agent Tooling (Authoring, Integrations, OAuth, Durable State), Quality & Evaluation (Text & Voice Tests, Evals, Regression, Traces), Agentic Engineering (Claude Code, Codex, QualityLayer, Hooks, Prompts), Context Engineering (RAG, Vector Stores, Codebase Embeddings), Software Delivery (CI/CD Integration, Agentic Loops, PR Automation), Enablement & Coaching (Workshops, Hands-On Labs, Pair Programming)
Michael F.
Last position:
Platform Architect (Project lead) at Würth IT
I designed and built a self-service AWS platform for Data Science teams with little or no infrastructure-as-code experience. A team writes about 10 lines of Python and gets a deployable stack with full SDLC release cycle that already follows the governance rules. Networking, encryption, IAM and tagging are decided by the platform, not by each team.
New teams or AWS accounts are onboarded with one pull request. Waste prevention and cost control is built into the defaults: Data scientists don't need infrastructure skills.
- Construct library (AWS CDK, Python). Built a versioned catalog of about 20 hardened constructs for being used in data-science projects with ML context
- Governance as code
- Multi-account, multi-tenant
- Golden-path scaffolding
- CI/CD and supply chain. Built reusable GitHub Actions workflows
- Release engineering
- QA with e2e, contract testing and unit test coverage with each release candidate
Harold T.
Last position:
CPU Watcher — Cloud-Native Monitoring Application at SEUYTEL
- Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
- Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
- Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
- Integrated PostgreSQL for persistent storage and management of application data.
- Containerized the application and its services using Docker Compose.
- Automated infrastructure provisioning and deployment using Terraform on AWS.
- Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
- Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
- Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
- Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
- Designed and implemented the frontend delivery architecture using AWS CloudFront.
Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Ales L.
Last position:
Senior DevOps Consultant (Freelance) at European Union Agency (via IBM)
- Worked as freelance Senior DevOps Consultant on-site for IBM at a European Union Agency, operating in a highly secure, air-gapped environment managing classified systems.
- Led automation and DevOps initiatives for a large-scale OpenShift platform (>400 nodes), driving deployment efficiency, GitOps adoption, and operational automation using Ansible, Python, and Bash while ensuring compliance with security requirements.
- Spearheaded automation of release and deployment workflows in a private cloud environment hosting 400+ OpenShift nodes, significantly improving deployment speed and reliability.
- Migrated existing playbooks, roles, and templates from Ansible Tower to Ansible Automation Platform (AAP), ensuring full compliance with fully-qualified collection names (FQCN) and preparing custom Execution Environments (EE) for containerized automation.
- Implemented GitOps Agent for AAP Controller Configuration as Code, enabling automated synchronization (CRUD) of Ansible Controller objects based on repository-stored configuration definitions using GitHub webhooks.
- Designed and automated complex multi-step operational workflows including environment cleanup, Helix cluster component re-creation, Kafka topic management, and OpenShift object lifecycle management across ~100 environments.
- Achieved a reduction of multi-day manual operations to under a few hours through automation improvements spanning multiple AAP clusters and OpenShift environments.
- Integrated Ansible Automation Platform with Thycotic (Delinea) Secret Server via lookup plugin to enhance secure credential management in automated processes.
- Managed deployment tasks, platform troubleshooting, and Istio network configurations while adhering to stringent EU PSC security and compliance standards.
- Collaborated with infrastructure and application teams to refine deployment procedures, develop naming conventions, and continuously improve automation coverage in an air-gapped, classified environment.
Karen M.
Last position:
Personal AI Engineering Project — Croky AI at Crocky AI
Product:
- Built a production-ready AI platform for generating brand-aware marketing images and videos from product data, user requirements, and uploaded media.
- Own the platform architecture, technical roadmap, API design, security, deployment workflow, operational reliability, and model-provider strategy.
- Developed the core platform in .NET and built supporting AI and workflow prototypes in Python, applying language-independent API contracts and structured interfaces between services and model providers.
- Implemented reliable background processing with RabbitMQ, persisted workflow state, idempotent handling, retries, failure recovery, logging, secure storage, authorization, and credit accounting.
- Made pragmatic build-versus-buy and model-routing decisions based on reliability, latency, cost, and maintainability rather than novelty.
Agent Orchestration & RAG Systems
- Built and compared agent workflows using Microsoft Agent Framework, LangGraph, and LangChain, including tool use, conditional routing, clarification steps, state management, and hand-offs between agents.
- Implemented reusable .NET components for agents, prompts, tools, model providers, structured responses, and retrieval with pyvector, making it easier to change AI providers without rewriting the core workflow.
Ramazan C.
Last position:
Fullstack-/DevOps Engineer at BKA (Federal Criminal Police Office)
Development and further development of an internal platform for managing and providing technical resources, virtual machines, and infrastructure services. The platform supports self-service processes and covers functions that are conceptually comparable to cloud management solutions like Azure or AWS.
- Responsible involvement in the design, development, and implementation of new backend and frontend features
- Hands-on development with Java, Spring Boot, Python, and Angular
- Implementation of REST interfaces, business logic, validations, and integrations into existing system landscapes
- Further development of modern web interfaces with Angular, including connection to backend services
- Participation in architecture and design decisions within the team, especially with regard to scalability, maintainability, and clean interfaces
- Containerization and deployment of applications with Docker, Kubernetes, and Helm
- Support with CI/CD processes and deployment to Kubernetes-based environments
- Work in the environment of vSphere, Broadcom, GitLab CI/CD, ArgoCD, Maven, npm, and NuGet
- Close collaboration with developers, business teams, DevOps, and other technical stakeholders
- Analysis of technical requirements, deriving suitable solutions, and independent implementation in an agile team
- Use of GitHub Copilot to support code generation, refactoring, test case creation, and technical documentation
Methods/ tools/ technologies: Languages & frameworks: Java (21), Spring Boot (4.x), Python, Angular, Robot Framework, Kubernetes, Helm Persistence: PostgreSQL, MongoDB, Hibernate, Liquibase Architecture & communication: REST, gRPC, GraphQL, Apache Kafka, OpenAPI, Microservices, Event Driven, Domain Driven Design Cloud & infrastructure: Terraform, Docker, Rancher, Helm, Ansible Security: OAuth2, MS (Entra ID), web security, Keycloak (extensions for detailed group rights) DevOps: GitLab CI/CD, Ansible, Maven, Gradle, Grafana, Prometheus, Git, GitHub Copilot Testing & QM: JUnit, Robot Framework, automated component and integration tests, E2E tests with Playwright, Testcontainers, EasyMock Methodology & approach: Kanban, JIRA, Confluence, Clean Code
Abdellatif L.
Last position:
Senior Software Engineer & Team Lead at Euvia Travel GmbH (sonnenklar.TV)
- Technical lead of a four-person development team during the two-year redevelopment of the sonnenklar.TV travel portal with over 10,000 daily visitors
- Replaced an established legacy architecture based on Laminas (Zend Framework) and Elasticsearch with a headless platform based on TYPO3 LTS and MariaDB — consolidated two systems into one and significantly reduced operational and maintenance effort
- Built the frontend independently with Vue.js, Nuxt, TypeScript and TailwindCSS; developed most of the component library independently
- Migrated the editorial workflow for over 30 editors from a custom-built backend to the TYPO3 backend
- Significantly reduced loading times for core pages; continuous delivery via Jenkins and GitLab CI
Position ended due to operational reasons as part of staff reductions following the decline in demand in the travel industry.
Aurelia H.
Last position:
Software Full Stack Developer at INNIO Jenbacher GmbH & Co OG
- Implementing and extending an Electron app based on TypeScript, React and Node.js to display stream content and technical data
- Extending a Kotlin backend for message processing via Kafka
- Extending various React modules
- Programming custom solutions for customers, including testing
- Creating documentation
- Development in accordance with Scrum processes
Used skills: React, React Hooks, Jest, Vitest, Typescript, Electron, IntelliJ, Vite, Github, Jira, Node.js, yarn, npm, pnpm, Swagger, OpenAPI, Storybook, Docker, Prettier, Eslint, tailwindcss, i18n, GraphQL, CoPilot, Postgres, JSON, Sqlite, trpc, influxdb, Java, Spring, Kotlin, Kafka, Git, Sonarcube
Niko S.
Last position:
Developing Architect, Technical Lead "gridlytics" at HH Energienetze
- Building a data integration platform for high, medium, and low voltage assets for contextual analysis of time series with master data from the SCADA control system (IEC 60870 104), INIS, and SAP.
- Responsibility for the architecture and implementation of the solution, as well as sparring partner for the Product Owner.
- Use of Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter, and various API gateways.
Sabahattin K.
Last position:
Sole responsibility (design, development, infrastructure, operations) at Own project busik.ch
- Ride-sharing and bus platform, live and fully functional. Backend with Spring Boot 4.1 on Java 21, PostgreSQL with Flyway, and Testcontainers integration tests. Hosted in my own AWS account (ECS Fargate, ALB, ECR, IAM Least-Privilege) with CI/CD via GitHub Actions and OIDC federation without static credentials. Development fully AI-supported with Claude Code, including custom skills and project-specific memory. Spring Boot · Java 21 · PostgreSQL · Flyway · Docker · AWS ECS/ALB/ECR · CI/CD · GitHub Actions · Claude Code
Henning U.
Last position:
Senior Expert Data Governance, Master Data Quality and Data Migration at E.ON
- Planning and implementation of a migration strategy for master and transaction data for the continuous loading of a cloud-independent database
- Creation and pilot implementation of a company-wide Business Data Model for customers, suppliers, contracts, products, prices, consumption, invoices, and dunning
- Concept and consulting for a Data Governance Framework incl. definition of committees, roles, processes, and metadata model
- Operationalization of the Data Governance Framework with definition of data standards
- Sub-project management in two pilot projects (PoCs) for Data Governance systems (ErwinDIS and Atlan)
- Training and coaching the data team in migration, data quality, and data modeling
Martin H.
Last position:
Lead Product Owner at Energy
- Team leadership: Prioritization and coordination of four cross-functional teams.
- Platform strategy: Development and implementation of strategies to optimize existing IT platforms.
- Stakeholder management: Active management of expectations and communication with internal and external stakeholders.
- Program and innovation management: Prioritization and coordination of cross-department projects as well as innovation initiatives.
- Product Owner consulting: Advising Product Owners with a focus on product development and continuous product improvement.
- Organizational development: Improving communication and decision-making structures across all organizational levels.
- Change management: Implementing best-practice change management methods to ensure continuous optimization and innovation.
- Quality assurance: Ensuring high quality standards in processes, services, and deliverables.
Gwen G.
Last position:
Senior Ruby on Rails & DevOps Developer at Sapiens
Further development of a software solution for managing audit applications and audit notifications according to §106d SGB V for the Association of Statutory Health Insurance Physicians in Hamburg.
- Developed numerous features, optimised performance, refactored code and analysed errors
- Refactored the development container setup
- Optimised the Gitlab-CI build pipeline
- Introduced improvements to the team workflow
Carsten R.
Last position:
Enterprise Product Owner at opta data IT GmbH
- Product responsibility for the central platform "one" as a group-wide web-based customer portal
- Coordination of the connection of 20 group companies to the product platform
- Derivation and steering of a group-wide product strategy and roadmap aligned with company goals
- Prioritization and bundling of strategic requirements from the various group companies
- Harmonization of different interests and moderation of complex decision-making processes at management level
- Ensuring the technical and business integration of the product into existing system landscapes, business processes, and business models
- Building transparent governance and decision-making structures for group-wide product development
- Representation of the product towards internal and external stakeholders at leadership level
Discover over 15,000 top freelancers
Statistics of experts using Elasticsearch
Aggregated from the professional profiles of matched freelancers.
Experience
18 years

Position duration
2 years

Positions per freelancer
12

Top business areas
Information Technology, Product Development, Operations

Top industries
Information Technology, Banking and Finance, Retail

Certification focus areas
Information Technology, Product Development, Project Management
Bachelor's degree or higher
92%
Master's degree or higher
50%
Doctorate
7%

Certifications per freelancer
2

Most common languages
German, English, French

Speak two or more languages
99%
Based on our profile pool as of 16 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts using Elasticsearch
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 16 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Elasticsearch experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (97%)
- Banking and Finance (48%)
- Retail (45%)
- Automotive (44%)
- Manufacturing (33%)
- Transportation (32%)
- Government and Administration (31%)
- Telecommunication (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Elasticsearch does
Elasticsearch is a distributed search and analytics engine built on Apache Lucene. It indexes structured and unstructured data so applications can return relevant results quickly, aggregate large datasets and detect patterns across changing information. It is commonly used behind product search, log analysis, observability and data discovery interfaces.
Search and analytics use cases
- Build full-text search for catalogs, documents, websites and knowledge bases
- Add filters, facets, autocomplete, highlighting and typo tolerance
- Analyze logs, metrics, traces and security events with Kibana
- Create dashboards and alerts from operational or business data
- Support semantic, vector and hybrid retrieval for AI-assisted applications
Ecosystem and tooling
Experts work with Elasticsearch Service, Elastic Cloud, Kibana, Logstash, Beats and Elastic Agent. They use the REST API and official client libraries for languages such as Java, Python, JavaScript and Go. Strong delivery also involves mappings, analyzers, ingest pipelines, index templates, aliases, snapshots and role-based access controls.
When companies need specialists
Companies often bring in freelance expertise when search quality is inconsistent, an index has grown difficult to operate or a migration from another search system is underway. Specialists can assess data models, tune queries, plan shard and replica strategies, and establish recovery procedures without disrupting production. They also help teams turn raw logs into useful observability workflows.
Skills that matter in delivery
A capable professional understands Lucene scoring, analyzers, tokenization, relevance testing and the trade-offs between text, keyword, numeric, date, geo and vector fields. They can profile slow searches, control refresh and storage behavior, and design mappings that remain stable as data changes. Experience with Docker, Kubernetes, cloud networking, CI/CD and security is valuable when Elasticsearch is part of a wider platform.
How to judge strong expertise
Look for clear reasoning about relevance, query cost, index lifecycle and failure recovery rather than a collection of copied configurations. A strong specialist explains why a mapping or analyzer fits the data, validates results with representative queries and documents operational decisions. They should also define monitoring, capacity signals, backup checks and a practical handover so the search service remains maintainable after delivery.
Frequently asked questions
Questions about Elasticsearch? Start with the answers below.
Elasticsearch is used for fast full-text search, filtering, faceting and analytics across structured and unstructured data. Companies also use it for log management, observability, security event analysis, recommendation features and vector or hybrid retrieval.
Elasticsearch is often compared with OpenSearch and Apache Solr because all three support Lucene-based search and analytics. The right choice depends on licensing, managed service options, existing integrations, query features, operational skills and the team’s preferred ecosystem.
A strong Elasticsearch specialist usually understands REST APIs, one or more official client libraries, data modeling and relevance testing. Useful adjacent skills include Kibana, Logstash, Elastic Agent, cloud networking, Kubernetes, monitoring, security controls and data pipeline design.
The needed experience depends on the scope. A simple application search feature may need strong API and mapping skills, while a production cluster, migration or observability system requires deeper knowledge of shards, replicas, lifecycle policies, recovery and performance under real workloads.
Elasticsearch projects can usually be delivered remotely when access, environments and sample data are documented clearly. Teams should agree on communication routines, incident procedures, data protection boundaries and time-zone coverage, especially when the specialist will change production search or observability systems.
Elasticsearch may be excessive for a small dataset that only needs simple relational queries or exact lookups. It also requires careful operations for high-volume workloads, so a simpler database search feature can be preferable when relevance, scale and analytics do not justify the additional system.
Ask the Elasticsearch specialist to explain mapping choices, analyzer behavior, relevance trade-offs and failure recovery in terms of your data. Good professionals test with representative queries, measure slow searches, document assumptions and include monitoring, backups and a clear handover.
Before starting, an Elasticsearch freelancer should clarify data volume and growth, query patterns, latency expectations, update frequency, availability needs and security constraints. They should also confirm the current version, hosting model, ingestion sources, deployment process and who owns production operations after delivery.
The average hourly rate of freelancers who have used Elasticsearch in their recent projects is 97 €, which corresponds to a daily rate of about 776 € based on an 8-hour working day.
Of the freelancers who have used Elasticsearch in their recent projects, 92% hold at least a Bachelor's degree, 50% hold at least a Master's degree, and 7% hold a doctorate.
On average, freelancers who have used Elasticsearch in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers who have used Elasticsearch in their recent projects are German (99%), English (98%), and French (17%).
The most common industries among freelancers who have used Elasticsearch in their recent projects are Information Technology (97%), Banking and Finance (48%), and Retail (45%).
The most common business areas among freelancers who have used Elasticsearch in their recent projects are Information Technology (100%), Product Development (93%), and Operations (54%).
Main locations of FRATCH Experts, who have recently used Elasticsearch
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Countries:
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