Azure Synapse Analytics Experts
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Meet FRATCH Experts who have recently used Azure Synapse Analytics
Umut Gülac
Last position:
Data Architect at BA Technology
I am an experienced data engineer specializing in end‑to‑end data integration, cloud DWH architectures, and high‑quality, governed data products.
I delivered following projects and engagements as a freelancer.
- Data Migration of CRM System for AL-FA Objekt Service Gmbh
- Microsoft Software Resales Partnership
I am looking for freelance roles like: Freelance Data Engineer Cloud Data Warehouse Architect Data Modeling & Architecture Consultant MDM & Data Governance Specialist BI & Analytics Developer
Technical Focus Areas
- Data Engineering & Integration: SQL Server/SSIS, Informatica PowerCenter/IDQ, Talend, Kafka, Azure Data Factory – Delta/CDC/ELT patterns, robust pipelines, monitoring/recovery, data lineage & impact analysis, medallion architecture Bronze/Silver/Gold layers
- DWH & Cloud: Azure SQL / Data Lake / Synapse, AWS Redshift/S3, on‑prem SQL/Oracle – scalable data marts with a strong cost/benefit focus.
- Data Modeling: Atomic (Inmon) and Dimensional (Kimball), Data Vault (Linstedt), Domain‑Driven Design, clear lineage & contracts.
- MDM & Governance: Informatica MDM, IBM MDM, stewardship processes, data quality rules, survivorship/XREF, catalog/glossary, SIF/BES/REST publication.
- Analytics/BI: Power BI, SSAS, Cognos – business‑ready, maintainable data products.
Alexander Zhirov
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.
Alexander Bromberg
Last position:
Senior Data Engineer at RWE AG
Architected and maintained data products for renewable energy operations, covering wind turbine, grid-meter, and weather data. Built scalable ETL/ELT pipelines in Azure Databricks using Delta Lake (bronze/silver/gold layers) and processed data in various formats, including structured and semi-structured data. Contributed to a data quality framework supporting table and column documentation, outlier detection, and completeness metrics across all datasets within a data product. In addition, implemented a DORA KPI Databricks dashboard used across all data products. Optimized CI/CD processes in Azure DevOps to streamline deployment across development, test, and production environments.
Technology stack: Azure Databricks, PySpark, SQL, Delta Lake, Unity Catalog, Azure Data Lake, APIs, Dremio, Azure DevOps, YAML, Git, Databricks Workflows, Application Insights, Terraform, OpenAI API, Codex, LLM-assisted workflows
Jorge Machado
Last position:
Technical Lead / Fractional CTO at Würth GmbH
I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.
Main Tasks:
- Sprint planning and feature preparation
- Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
- Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
- Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
- Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
- Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
- Manage production releases and execute live data migrations for enterprise customers
- Define engineering standards and architecture patterns for the team
Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL
Tobias Lewen
Last position:
Data Engineer at unitb consulting GmbH
Tasks: Design and operation of end-to-end cloud data platforms for enterprise clients in publishing and finance, including infrastructure automation, pipeline development, monitoring, and data quality.
Activities:
- Built multi-layer data architectures on Databricks (Apache Spark, Delta Lake), BigQuery, and GCP
- Fully automated cloud infrastructure with Terraform across 3 environments (DEV/STG/PRD)
- Developed automated data pipelines with Python, dbt, and GCP services for different data sources
- Built monitoring and alerting systems for real-time platform monitoring
- Implemented data versioning and quality checks at every layer
- Designed automated test and deployment pipelines in GitLab and Bitbucket
Achievements:
- 2× production data processing capacity, reduced spike response time from minutes to ≤15 s, server errors ≈ 0
- Replaced 3,000 lines of manual configuration with a reusable automation module for 7 customer domains, configuration errors to 0
- Delivered a complete end-to-end data platform at ~€10/month infrastructure cost
- Migrated 7 database tables with 0 downstream issues
- Removed 100% exposed credentials, eliminated external vendor dependency
- Delivered integration of 3 teams in 1 sprint
Jan Krol
Last position:
Data Expert at Manufacturing
Tan Pham
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Minal Borse
Last position:
Business Intelligence Specialist at Coster Special Technologies S.p.A.
- Designed and developed interactive SAP Analytics Cloud (SAC) dashboards and reports for Finance, Supply Chain, Logistics, Procurement, HR, and Manufacturing, covering KPIs such as Profit & Loss, Balance Sheet, Fixed Costs, Headcount, Personnel Expenses, Stock Analysis, OTIF, Production Volume, BOM, Spend, and Compliance to Schedule.
- Built and optimized end-to-end ABAP CDS data models (Basic, Composite, and Consumption Views) using the VDM approach, integrating data from key SAP S/4HANA tables. Strong expertise in ABAP CDS, SQL, SAP data modeling,
- Collaborated with cross-functional teams to define KPI logic, standardized user story templates, resolved BI requests through JIRA, improved reporting performance, and delivered scalable, secure, and business-focused analytics solutions that enhanced decision-making and operational efficiency.
- Trained business stakeholders across various countries on SAP Analytics Cloud (SAC) dashboard usage and developed comprehensive training manuals to promote user adoption and enable self-service analytics.
Carlbandro Edoga
Last position:
IT Lecturer / Coach at Freelance
As a freelance IT lecturer and IT coach, I specialize in teaching individuals concepts like Cloud Computing, Business Intelligence, Python Programming and Agile Frameworks. My goal is to simplify complex technical concepts into practical, actionable knowledge. Through my extensive experience as an IT consultant in various sectors and roles I know about the importance of IT training - for companies and employees alike.
Manikanta Rangaswamy
Last position:
Data Engineer at Insurance client
- Design, development, and maintenance of end-to-end ETL pipelines for scalable and reliable data integration
- Support in data quality checks, testing, and migrations
- Development and maintenance of dbt models for structured, modular, and reusable data transformations
- Use of AI-driven development to improve ETL job creation and code quality.
- Development of CI/CD for automated deployment.
Hardeep Bhutter
Last position:
Sr. Data Engineer at Charles Schwab Bank
- Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
- Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
- Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
- Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
- Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
- Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
- Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
- Created and maintained dashboards in Power BI to provide actionable insights.
Olga Methner
Last position:
Telefónica Deutschland Holding AG
- Implemented the BI solution and replaced the old BI landscape
- Led the “Sales Bonus Plan” subproject: expanded the existing solution and migrated to Azure (Databricks)
- Led the “Cognos Migration” subproject: expanded existing data warehouses based on MS technologies (source systems: Oracle), analyzed business requirements and designed the technical solution
- Expanded the relational DWH database
- Developed ETL processes using SSIS
- Developed the multidimensional database (OLAP Cubes/SSAS)
Ashkan Zadeh
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Oliver Köhn
Last position:
Consultant for data-driven AI solutions at Oliver Köhn - IT-Freelancer
- AI-powered automation with a focus on efficiency, information processing, and assistant systems
- Automated email classification (OpenAI, FastAPI)
- Contract analysis for LegalTech (Llama 3, LangGraph)
- Internal knowledge search with RAG (VLLM, Hugging Face)
- Anomaly detection on edge devices (LLAVA, TensorRT)
- Agent system for management reports (LangGraph, Zapier)
Nima Nooshi
Last position:
Co founding LLM Engineer at LLM Ventures
- Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
- Designed and implemented multi-agent AI workflows for financial and trading applications
- Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
- Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
- Led system architecture decisions across model selection, orchestration, state management, and deployment
Discover over 15,000 top freelancers
Statistics of experts using Azure Synapse Analytics
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2 years
Positions per freelancer
12
Top business areas
Information Technology, Business Intelligence, Project Management
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
96%
Master's degree or higher
64%
Doctorate
12%
Certifications per freelancer
5
Most common languages
English, German, French
Speak two or more languages
97%
Based on our profile pool as of 6 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology 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 using Azure Synapse Analytics
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 6 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Synapse basics
Azure Synapse Analytics is a cloud service for analytics engineering, data warehousing, and big data processing in one workspace. Teams use it to query data with SQL, run Spark jobs, and orchestrate pipelines across Azure data services. It is often shortened to Synapse and still comes up in searches under Azure SQL Data Warehouse.
Typical work
- Build lakehouse-style analytics layers on top of Azure Data Lake Storage
- Create serverless and dedicated SQL solutions for reporting and ad hoc analysis
- Orchestrate ingestion and transformation with Synapse pipelines
- Prepare curated datasets for Power BI and downstream analytics
- Connect Azure Synapse Analytics to governance, security, and monitoring tools
Skills that matter
Strong specialists know SQL, Spark, data modeling, and Azure identity and access control. They understand partitioning, distribution, performance tuning, and how to move data cleanly between storage, compute, and BI layers. Experience with Python, T-SQL, and Azure services around Synapse helps them deliver stable solutions.
When companies bring help
Teams usually look for freelance experts when a warehouse is slow, ingestion is unreliable, or a migration from Azure SQL Data Warehouse needs careful handling. They also bring in support when they want to combine batch and exploratory analytics in one place without rebuilding the whole stack. The right specialist can review architecture, fix bottlenecks, and leave the team with better patterns.
Ecosystem fit
Synapse often sits beside Azure Data Lake Storage, Azure Data Factory, Key Vault, Microsoft Purview, and Power BI. In many projects, the work is not only about queries but also about access, lineage, and handoffs between data engineering and reporting. Good professionals know where Synapse ends and the surrounding Azure services begin.
What good looks like
Look for clear design choices, clean SQL, practical Spark usage, and a habit of testing pipelines before release. Strong professionals explain trade-offs between serverless and dedicated compute, and they can keep costs, performance, and maintainability in balance. They also document what they built so the next team can run it confidently.
Frequently asked questions
Need clarity? These are the questions we hear most often about Azure Synapse Analytics.
Azure Synapse Analytics is used for warehouse workloads, big data processing, and analytics pipelines in Azure. Companies use it to query structured and semi-structured data, prepare reporting datasets, and combine SQL and Spark in one workspace. It is a fit when the goal is to centralize analytics without splitting work across too many tools.
Azure Synapse Analytics includes what many teams knew earlier as Azure SQL Data Warehouse, but the service has grown beyond classic warehouse use. Today it also covers Spark, pipelines, and broader analytics orchestration. When people still say Synapse SQL or Azure SQL Data Warehouse, they are often referring to the same core warehouse capability.
Azure Synapse Analytics is usually chosen when SQL warehousing, pipelines, and Azure-native integration matter most. Databricks is often favored for more advanced notebook-driven data engineering and machine learning workflows. Many teams use both, with Synapse handling warehouse and orchestration tasks and Databricks covering more specialized processing.
A strong Azure Synapse Analytics specialist usually brings solid SQL, Spark, and data modeling skills. Azure Data Lake Storage, Power BI, security, and pipeline orchestration are common adjacent skills. Python and T-SQL are also useful when the work spans both transformation logic and warehouse tuning.
Azure Synapse Analytics projects vary a lot, but they usually need someone who has already shipped real warehouse or pipeline work. Simple reporting setups are easier, while migrations, performance tuning, and mixed SQL-Spark designs need deeper experience. If the project touches identity, governance, and production data, do not rely on surface-level familiarity.
Yes, Azure Synapse Analytics work is often done remotely because most tasks happen in Azure and in code. On-site collaboration can still help when teams need access to local stakeholders, security reviews, or tight coordination with data owners. Many companies use a hybrid setup and keep the implementation remote.
A good Azure Synapse Analytics expert can explain architecture choices in plain language and show how they handled performance, reliability, and security. Look for clear examples of SQL tuning, pipeline design, and working with Azure storage and access controls. Clean documentation and a calm approach to trade-offs are strong signs of quality.
Azure Synapse Analytics projects often run into trouble when teams move data without a clear model or when they choose the wrong compute pattern for the workload. Slow queries, poor partitioning, weak access control, and fragile pipelines are common pain points. A skilled specialist spots those issues early and keeps the design simple enough to operate.
The average hourly rate of freelancers who have used Azure Synapse Analytics in their recent projects is 103 €, which corresponds to a daily rate of about 825 € based on an 8-hour working day.
Of the freelancers who have used Azure Synapse Analytics in their recent projects, 96% hold at least a Bachelor's degree, 64% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers who have used Azure Synapse Analytics in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers who have used Azure Synapse Analytics in their recent projects are English (100%), German (97%), and French (18%).
The most common industries among freelancers who have used Azure Synapse Analytics in their recent projects are Information Technology (88%), Banking and Finance (58%), and Automotive (45%).
The most common business areas among freelancers who have used Azure Synapse Analytics in their recent projects are Information Technology (100%), Business Intelligence (91%), and Project Management (58%).
Main locations of FRATCH Experts, who have recently used Azure Synapse Analytics
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.
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Philipp Thomaschewski
FRATCH CEO
