Great Expectations Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Great Expectations
Serge K.
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
Lasya M.
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.
Prajwal A.
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 K.
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 P.
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 Y.
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 19 Sep 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Great Expectations 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 (100%)
- Retail (50%)
- Education (33%)
- Banking and Finance (33%)
- Food and Beverage (33%)
- Healthcare (33%)
- Insurance (33%)
- Professional Services (33%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Data quality checks Great Expectations is used to define and run expectations for data quality. It helps teams verify columns, values, ranges, schema changes, null handling, and distribution shifts before bad data reaches dashboards, models, or downstream systems.
Typical use cases
- Validate batch pipelines after ingestion or transformation
- Check warehouse tables before reporting or activation
- Guard machine learning feature data and training sets
- Spot schema drift in evolving source systems
Stack and tooling It fits into Python-first data workflows and often sits beside pandas, SQL, Airflow, dbt, Spark, and cloud warehouses. Strong specialists know how to write readable expectation suites, manage checkpoints, and keep validation outputs useful for analysts and data teams.
When companies need help Teams usually bring in freelance expertise when data tests are missing, inconsistent, or hard to maintain. They also need support during pipeline rebuilds, warehouse migrations, and quality rollouts across many datasets, especially when the same standards must work across several domains in Germany or remotely with mixed teams.
What strong specialists do Good professionals do more than add checks. They map business rules to technical validations, tune suites so they fail for the right reasons, and make results easy to act on. They also document assumptions, review source data quirks, and keep quality logic aligned with real pipeline behavior.
Collaboration and delivery Great Expectations work is often delivered as part of data engineering, analytics engineering, or governance projects. Freelancers may set up reusable expectations, connect validations to CI or scheduled runs, and help teams decide what should be blocked, warned on, or simply observed.
Frequently asked questions
Need clarity? These are the questions we hear most often about Great Expectations.
Great Expectations is used to test whether data still matches the rules a team expects. It is common in batch pipelines, warehouse loads, and analytics workflows where bad rows, missing fields, or schema changes can break reporting or downstream processing.
Great Expectations is broader than simple SQL tests in dbt because it can express richer validation patterns and produce structured results for many kinds of data. Compared with custom Python checks, it gives teams a clearer framework, reusable suites, and a more standard way to document quality rules.
A strong Great Expectations specialist usually knows Python, SQL, and data pipeline design. Familiarity with Airflow, dbt, Spark, pandas, and cloud warehouses helps because the quality checks must fit the way the data actually moves.
Great Expectations projects can be simple or complex depending on the number of pipelines and the quality rules involved. A small setup may need only a few focused expectation suites, while a broader rollout needs someone who can design maintainable patterns and avoid brittle checks.
Yes, Great Expectations work is often done remotely because the core tasks are code, data access, and review of validation results. For teams in Germany, remote collaboration is common, but some projects still benefit from overlap with local data or analytics teams when source systems are messy or undocumented.
Look for someone who writes clear expectations, understands the pipeline context, and can explain why each check matters. A good Great Expectations professional also keeps suites maintainable, uses sensible naming, and shows how failures will help teams act quickly.
Great Expectations is most often used in Python-based workflows, but it can support wider data stacks through SQL and pipeline integrations. The key is not the language alone; it is whether the specialist can place validation at the right step in the workflow.
Great Expectations is a better fit when data changes often, several teams rely on the same tables, or failures must be caught before users see them. Manual review is still useful for edge cases, but it does not scale well once pipelines, sources, and stakeholders grow.
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
