Great Expectations Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Great Expectations
Lasya Marella
Last position:
Data Engineer at Carelon Global Solutions (Elevance Health)
- Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
- Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
- Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
- Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
- Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
- Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
- Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.
Serge Kalinin
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Prajwal Amoghavarsh
Last position:
Master Thesis at Smart City Research Lab
From Crude to Crafted: Refining Participatory Design Data into Stakeholder-Ready Outcomes
- Architected a production Document AI platform using Retrieval Augmented Generation (RAG) over 1,500+ participatory design artefacts to answer historical project queries with grounded responses.
- Designed LLM evaluation combining RAGAS, custom evaluation metrics and human-in-the-loop (HITL) validation workflows to evaluate factual grounding, response quality, and prompt performance.
- Built a React, TypeScript, and D3.js frontend for interactive exploration of AI-generated insights.
- Implemented input layer LLM safety controls and Guardrails, including PII redaction and foul language filtering.
Maziyar Khorrami
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Daniel Pape
Last position:
Professional Development
Attained AWS Certified Cloud Practitioner certification.
Mastered Rust through self-study, including books, online courses, and open-source contributions.
Developed a serverless web application using AWS (RDS, Lambda, Polly, Amplify) and TypeScript/React/D3, managed infrastructure with CDK.
Continuously stayed updated with industry trends through self-education, webinars, and workshops, exploring Data Mesh and FastAPI.
Aqsa Younus
Last position:
Multilingual Translation Tool - NLP Project
- Integrated MarianMT (Marian Machine Translation) models to ensure high-quality neural machine translation (NMT).
- Managed model loading and tokenization via Hugging Face Transformers, optimizing for offline caching and reproducibility.
- Planned extensions: language auto-detection, batch translations, and streamlined GPU inference with PyTorch.
Discover over 15,000 top freelancers
Statistics of experts using Great Expectations
Aggregated from the professional profiles of matched freelancers.
Experience
10 years
Position duration
1.3 years
Positions per freelancer
8
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Information Technology, Retail, Education
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
33%
Certifications per freelancer
5
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using Great Expectations
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Data quality checks
Great Expectations is used to define expectations for tables, files, and records so teams can catch bad data before it reaches dashboards, models, or customer workflows. It fits batch pipelines, lakehouse jobs, and repeatable validation steps in CI.
What specialists deliver
- expectation suites for source and transformed data
- validation reports that show failed rules clearly
- automated checks in ETL and ELT pipelines
- documentation that explains data contracts and assumptions
Ecosystem fit
Strong professionals know pandas, SQL, Python, and the systems around the tool, including Airflow, dbt, Spark, and object storage. They also understand how to organize tests, reuse checkpoints, and keep validations readable for analysts and platform teams.
When to bring help in
Companies usually look for freelance expertise when data checks are missing, pipelines break often, or teams need to turn manual QA into a repeatable process. In Germany, this often comes up in data-heavy product, finance, and industrial settings where reliable reporting matters and teams may work in English or mixed-language setups.
What strong experts do
A good Great Expectations specialist does more than write rules. They choose the right test boundaries, balance strict checks with practical tolerances, and make sure failures point to the real source of the problem. They also keep validation code maintainable as schemas and upstream systems change.
Common search terms
People often search for Great Expectations, GE, or Great Expectations Cloud when they need data validation support. They may also look for someone who can review an existing suite, improve pipeline quality gates, or connect checks to a broader data platform.
Frequently asked questions
Need clarity? These are the questions we hear most often about Great Expectations.
Great Expectations is used to validate data before it is trusted by reports, pipelines, or models. It helps teams define rules for schema, null values, ranges, uniqueness, and other quality checks, then surface clear results when data does not meet those rules.
Great Expectations is usually chosen when teams want richer validation reports, reusable suites, and checks that can run across different data sources. dbt tests are strong for warehouse-centric transformations, while custom SQL can be quick but harder to maintain as requirements grow.
A strong Great Expectations specialist usually knows Python, SQL, and the data stack around the tool. Useful adjacent skills include Airflow, dbt, Spark, pandas, and practical experience with schema management, lineage, and pipeline troubleshooting.
A Great Expectations expert can often start with a focused review of one pipeline or dataset and expand from there. The more useful context you provide, the faster they can define sensible checks, map critical fields, and avoid brittle rules that create noise.
Yes. Great Expectations works well for batch files, warehouse tables, and staged transformations, as long as the specialist designs checks around the actual flow of data. It is especially useful when validation needs to happen at specific handoff points between systems.
Look for clear expectation suites, readable failure output, and checks that match business risk instead of copying every possible rule. In a solid Great Expectations setup, the specialist explains why each test exists and how it protects downstream work.
Usually not. Great Expectations work is often done remotely because it centers on code, data access, and pipeline review, though on-site sessions can help when teams need to align with local stakeholders or discuss data ownership and operating rules.
Yes, because Great Expectations focuses on explicit validation rules and controlled checks, which can complement observability tools rather than replace them. Many teams use it to enforce known quality requirements while observability covers broader monitoring and anomaly detection.
The average hourly rate of freelancers in Germany who have used Great Expectations in their recent projects is 95 €, which corresponds to a daily rate of about 756 € based on an 8-hour working day.
Of the freelancers in Germany who have used Great Expectations in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Germany who have used Great Expectations in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.3 years.
The most common languages among freelancers in Germany who have used Great Expectations in their recent projects are German (100%), English (100%), and French (33%).
The most common industries among freelancers in Germany who have used Great Expectations in their recent projects are Information Technology (100%), Retail (50%), and Education (33%).
The most common business areas among freelancers in Germany who have used Great Expectations in their recent projects are Business Intelligence (100%), Information Technology (100%), and Product Development (83%).
Main locations of FRATCH Experts, who have recently used Great Expectations
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.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
