
Amazon Athena Experts in Berlin
for fast, precise AI matching with vetted and available freelancersHire experts who query data directly in Amazon S3, shape serverless analytics with AWS Glue and publish results through BI tools such as Amazon QuickSight. FRATCH matches you quickly with precise, vetted and available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Amazon Athena
Vili D.
Last position:
Technical Lead, Data Engineer at Mercedes-Benz Consulting
Optimized the data architecture (Medallion) to better decouple processing steps and improve transparency and reproducibility
Ensured the technical quality of data processing in Databricks by introducing schema enforcement, data quality checks, and a structured data architecture
Pipeline orchestration with Azure Data Factory
Improved and automated the development and deployment process by integrating Git and GitHub Actions
Technical leadership of the Data Engineering team (3 employees)
Led workshops to optimize and stabilize the data platform and development process
Collected and prioritized new requests and maintained the product backlog
Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS Sharepoint, Mural
Mansharan K.
Last position:
MS Fabric | Power BI Consultant at Gropyus Technologies GmbH
- Architected an enterprise Fabric-based data-product governance framework, authoring data contracts and schema-validation KPIs to enforce completeness, freshness and SLA standards across reporting data products.
- Operationalized pipeline observability by integrating Dagster orchestration data into automated data-health reporting, proactively validating data integrity and pipeline reliability before production reporting layers refresh.
- Established BI-as-Code and end-to-end governance through Git-based version control, CI/CD and Alpha→GA release management, strengthening lineage, schema-drift detection and production change traceability.
Alexander Z.
Last position:
Senior Data Solutions Engineer at VMware Inc.
- Architected and deployed private cloud data platform on VMware vSphere, integrating Greenplum MPP, Apache Kafka, Kubernetes, and Apache Solr, and developed real-time ingestion pipelines with Kafka Connect and Schema Registry.
- Led Oracle Exadata to Greenplum migration, rearchitected data models, optimized storage, implemented RabbitMQ with Debezium for CDC, and deployed VectorDB for Generative AI.
- Designed and executed multi-cloud migration PoC across AWS, Azure, and GCP, defined KPIs for throughput, latency, and cost efficiency, executed bulk data transfers, validated analytics and streaming workloads, and delivered full-scale architecture recommendations.
- Assessed legacy on-premises infrastructure and designed modern cloud-native data platforms using Greenplum and containerized microservices, advising on scalability, disaster recovery, and high-availability.
Bidya B.
Last position:
Product Manager – Payments & Platform at Pipedrive
CRM and revenue platform managing billing and subscription workflows.
- Scaled payments infrastructure across data products, direct debit expansion and automated abuse prevention, generating $416K in annualized operational savings ($8K/week) by eliminating redundant gateway calls.
- Owned backlog and sprint execution for autonomous checkout abuse detection pipelines, designing real-time risk guardrails and velocity heuristics that blocked card testing attacks.
- Architected enterprise billing migrator user stories and data reconciliation mechanisms, achieving zero-downtime subscription state transitions and cutting $60K in infrastructure overhead.
- Expanded European direct debit (SEPA) payment capabilities, managing cross-squad API dependencies and automated webhook error-handling to eliminate checkout friction.
Gyan P.
Last position:
Senior DevOps & Cloud Architect, Head of Infrastructure at Bosch
- Azure Landing Zone architecture: Design and implement a hub-spoke, multi-region-capable Azure landing zone that provides the secure, scalable and highly available foundation for autonomous driving and parking data/ML platforms.
- Compliance through Azure Policy: Enforce security and compliance standards across Azure subscriptions via Azure Policy (guardrails, deny/audit/deploy-if-not-exists), consistently achieving top-tier infrastructure security scores.
- Infrastructure-as-Code: Automate provisioning and configuration of Azure resources with Terraform and Bicep, with changes delivered through reviewed CI/CD pipelines.
- Azure Databricks and Unity Catalog: Extensive experience operating Azure Databricks with Unity Catalog for centralized data governance, access control, lineage and auditing across workspaces.
- Monitoring and observability: Built and run infrastructure monitoring and logging with Azure Monitor, Prometheus, Grafana and Loki, covering platform health, alerting, troubleshooting and capacity trends.
- Platform at scale: Architected and operate cloud data/ML platforms supporting multi-PB vehicle data ingestion and simulation; orchestrate Argo Workflow-based recompute and autolabeling services scaling to 100k+ concurrent pods on AKS.
- Reproducible ML: Established reproducible training environments with dataset versioning and provenance tracking, enabling auditing and experiment reliability.
- Operational excellence: Automated GitHub organization management and CI/CD pipelines, improving developer productivity and deployment reliability; automated recurring operational processes.
- FinOps: Developed transparent resource utilization and cost-tracking for multi-project Azure environments.
- Leadership: Technical lead for infrastructure topics, collaborating with development, DevOps and security teams; technical interviewer and recruitment panel member for scaling the engineering team.
Jan K.
Last position:
Data Expert at Manufacturing
Salvation O.
Last position:
Product Management Consultant at Self-Employed (Freelancer)
- Led end-to-end product discovery and strategy engagements for early-stage founders (pre-seed) clients, defining product vision, OKRs, and go-to-market strategies and roadmaps for their cloud-based and AI-enabled solutions.
- Designed scalable product operating models (Agile/Scrum, backlog governance, KPI frameworks) as part of the partnership, to improve delivery predictability and reduce feature cycle time by up to 30%.
- Built scalable product roadmaps aligned to fundraising milestones, helping founders articulate product vision, product<>market fit, and traction clearly to investors.
Recep C.
Last position:
Sr. Software Engineer at Endava
- Designed and deployed scalable REST APIs for enterprise clients
- Led migration of legacy systems to modern maintainable system, reducing deployment time and enabling daily deployments
- Mentored junior developers and collaborated cross-functionally to align technical roadmaps with business goals, improving team velocity by 50%.
- Tech Stack: PHP (Symfony), Golang, PostgreSQL, MySQL, GCP, Terraform, Kubernetes
Christian R.
Last position:
Freelance Data Engineer at Ingenieurbüro Christian Richter – Data, Cloud & Container
- Contributed to over 20 successful projects
Ilya I.
Last position:
Data/Platform/Software Engineer/SRE at IT Consulting
- Designed a platform based on IoT, Azure, Kubernetes, and Postgres for an existing application
- Migrated from "click-ops" and UI-defined CI/CD pipelines to infrastructure-as-code with Terraform, enabling complete redeployment of multiple environments
- Technologies: Terraform, OpenTofu, Azure, Azure DevOps, Kafka, IoT, Kubernetes, Grafana, Prometheus, GitOps, relational databases
Discover over 15,000 top freelancers
Statistics of experts using Amazon Athena
Aggregated from the professional profiles of matched freelancers.
Experience
16 years (Germany: 15 years)

Position duration
2.5 years (Germany: 2 years)

Positions per freelancer
8 (Germany: 9)

Top business areas
Information Technology, Business Intelligence, Product Development

Top industries
Information Technology, Automotive, Banking and Finance

Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
89% (Germany: 93%)
Master's degree or higher
33% (Germany: 53%)
Doctorate
11%

Certifications per freelancer
3 (Germany: 4)

Most common languages
English, German, Hindi

Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Berlin 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 in Berlin using Amazon Athena
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Amazon Athena 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 (80%)
- Automotive (40%)
- Banking and Finance (40%)
- Media and Entertainment (40%)
- Professional Services (40%)
- Retail (30%)
- Manufacturing (20%)
- Tourism (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Serverless queries
Amazon Athena is a serverless, interactive query service for analyzing data stored in Amazon S3. It uses standard SQL and removes the need to provision or maintain database servers. Companies use it for data lakes, ad hoc analysis, operational reporting and exploration of large structured or semi-structured datasets.
Data lake foundations
Athena works with formats such as Parquet, ORC, JSON and CSV, while AWS Glue Data Catalog provides table definitions and metadata. Strong implementations organize S3 prefixes, partitions and schemas so queries remain understandable and efficient. Knowledge of IAM, encryption, workgroups and AWS cost controls is part of the wider skill set.
Analytics ecosystem
Amazon Athena connects with services and tools across the AWS analytics stack:
- AWS Glue crawlers, Data Catalog and ETL workflows
- Amazon S3 data lakes and Lake Formation permissions
- Amazon QuickSight dashboards and BI reporting
- JDBC or ODBC integrations for applications and notebooks
- Apache Spark, Apache Iceberg and other table technologies
When expertise matters
Companies bring in freelance specialists when an S3 data lake needs a usable query layer, when slow scans raise costs, or when business teams need trusted reporting. They may also need help migrating from a warehouse, designing federated queries or connecting Athena with existing pipelines. In Berlin, remote delivery often works well, while on-site workshops can support data governance and stakeholder alignment.
Quality signals
A strong Amazon Athena professional designs schemas around real query patterns instead of treating S3 as an unstructured file store. They understand partition projection, columnar formats, compression, workgroup controls and query execution plans. They also document ownership, data quality checks and access rules so the solution can be operated after handover.
Choosing the right specialist
Look for experience with the complete path from source systems to dashboards, not only isolated SQL queries. Ask for examples involving Glue Catalog, Lake Formation, Iceberg or QuickSight when those components match your environment. Clear communication in English, and German where required for local teams, helps remote and on-site collaboration in Berlin stay productive.
Frequently asked questions
Questions about Amazon Athena? Start with the answers below.
Amazon Athena is used to run SQL queries directly against data in Amazon S3 without managing database servers. Companies use it for data lake exploration, reporting, log analysis, federated queries and scheduled analytics.
Amazon Athena queries data in place, which can reduce infrastructure management and suit irregular analytical workloads. A managed warehouse may offer more predictable performance for frequent, highly concurrent queries and complex transformation workflows.
A strong Amazon Athena specialist usually understands Amazon S3, AWS Glue Data Catalog, IAM and Lake Formation. Experience with QuickSight, data pipelines, Apache Iceberg and infrastructure automation is valuable when the work extends beyond query design.
Amazon Athena work can be straightforward for a small reporting task, but production data lakes need deeper expertise. The specialist should be able to assess schemas, partitions, file formats, permissions, query costs and operational ownership before recommending an approach.
Yes. Amazon Athena projects are well suited to remote collaboration because the work is managed through AWS accounts, repositories and shared documentation. Berlin teams may still prefer on-site workshops for data ownership, security reviews or requirements involving German-speaking stakeholders.
Amazon Athena may be unsuitable when an application needs consistently low-latency transactions or intensive concurrent queries with strict performance targets. A specialist should compare it with a warehouse, relational database or another query engine based on workload, governance and operating requirements.
Ask how the specialist would choose file formats, partitions, table layouts and access controls for your data. A capable Amazon Athena professional explains trade-offs clearly, validates query plans, controls scan volume and leaves reproducible documentation rather than relying on trial and error.
Professionals working with Amazon Athena often collaborate across data engineering, security and analytics teams. Practical work may include SQL, Glue metadata, S3 layout, Lake Formation permissions, CI/CD and BI integration, so adjacent AWS knowledge can be as important as query syntax.
The average hourly rate of freelancers in Berlin, Germany who have used Amazon Athena in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Amazon Athena in their recent projects, 89% hold at least a Bachelor's degree, 33% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Amazon Athena in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Berlin, Germany who have used Amazon Athena in their recent projects are English (100%), German (90%), and Hindi (30%).
The most common industries among freelancers in Berlin, Germany who have used Amazon Athena in their recent projects are Information Technology (80%), Automotive (40%), and Banking and Finance (40%).
The most common business areas among freelancers in Berlin, Germany who have used Amazon Athena in their recent projects are Information Technology (100%), Business Intelligence (90%), and Product Development (70%).
Main locations of FRATCH Experts, who have recently used Amazon Athena
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!

Munich