Apache Spark Expert
to scale data pipelines with fast, precise AI matchingHire experts who build distributed data pipelines, optimize Spark SQL workloads and connect Apache Spark with cloud data platforms, streaming systems and machine learning workflows. FRATCH matches you quickly with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts who have recently used Apache Spark
William N.
Last position:
Power BI Solutions Architect/Engineer & AI Consultant at AVERDUNG GmbH
- Redesign of the company’s BI infrastructure: replacement of a fragmented landscape of manually maintained Excel solutions and CSV imports with a centralized Power BI environment using a unified data model as the company-wide single source of truth
- Consolidation of previously isolated reporting logic in a central semantic model – elimination of redundant files, manual data transfers and inconsistent metrics across departments
- Forecasting & Planning: Design and implementation of company-wide liquidity planning in Power BI – from business logic to a fully automated, data-source-driven planning model replacing the previous manual Excel process; enabling rolling forecasts and continuously up-to-date cash flow transparency for management
- Optimization of existing Power BI dashboards in terms of performance, structure and analytical value using an AI-native approach
- Analysis and improvement of the data model, including data quality analyses, data cleansing and consistent modeling using Star Schema, DAX and Power Query
- Incident & Anomaly Analysis: Identification, investigation and explanation of data anomalies, including root-cause analysis and specific recommendations for action
- AI Solution Architecture: Connecting Business Central and Power BI to LangDock via MCP (Model Context Protocol) for AI-supported data usage
- Development of a historical data layer as a basis for trend and time-series analyses
- AI-supported automation: Design and development of AI skills, agents, loops and processes for the automated analysis and interpretation of reports
- Automated reporting workflow: Setup of scheduled, automated email delivery of AI-generated analyses and recommendations to stakeholders
- Gathering and documentation of business requirements and coordination with business departments and IT as part of requirements engineering / product owner activities
- Breaking down overall requirements into clearly defined work packages and tasks
- Definition, prioritization and management of milestones throughout the entire project lifecycle
Tools: POWER BI, M365, Copilot Studio, MIRO, Microsoft Business Central, Microsoft Fabric, Claude AI, ChatGPT, LangDock, MS VS Code
Fadi S.
Last position:
Data Scientist at E-Commerce
- Designed and developed a production-ready data platform based on Microsoft Fabric for an e-commerce company
- Built a scalable Medallion Architecture (Bronze, Silver, Gold) with Lakehouse and Delta Lake as the foundation for analytics and AI applications
- Developed a metadata-driven ETL platform with PySpark and a generic Incremental Loading Framework (SQL Server → Bronze → Silver → Gold)
- Developed reusable components for Microsoft Fabric Notebooks to standardize data integration, transformation, and validation
- Implemented automated data pipelines including monitoring, logging, error handling, and Delta MERGE strategies for stable production operations
- Built a business layer architecture for reporting, data warehousing, and reusable data products
- Integrated heterogeneous data sources via SQL Server, REST APIs, Amazon SP API, and Google Analytics 4
- Worked closely with business departments to analyze business requirements and translate them into scalable data architectures
Technologies: Microsoft Fabric, PySpark, Python, SQL Server, Delta Lake, Lakehouse, OneLake, Data Factory, Data Warehouse, REST APIs, Amazon SP-API, Google Analytics 4
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
Umut G.
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.
Hachem S.
Last position:
Machine Learning & BI Lecturer at EU Business School
- Taught ML, BI, and Big Data to Master's students.
Tech: Python, Power BI, SQL, ML, SnowflakeDB, databricks, MS Fabric, MongoDB, LLM
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
Jens H.
Last position:
Interim CTO (occasional assignments) at Fujitsu / FSAS
Stabilization of an Azure/.NET landscape in live operation.
- Architecture, DevOps, and operational readiness; technical decisions under time pressure
- Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics
Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps
Ajay C.
Last position:
Software Engineer & Cloud AI Developer at TANGILITY GmbH
Built Python-based AI microservices and integrations for an AEC/VR Unity-based SaaS app, focusing on LLM/VLM capabilities, retrieval-backed systems, RESTful APIs, containerized deployment, and an automation microservice for the CAD-to-Unity pipeline.
- Developed a custom Hybrid A* based algorithm in C# to simulate hospital scenarios, detect early-stage design conflicts using collision/spatial data, and generate structured reports.
- Solved and automated the time-consuming task of converting CAD files into usable Unity environments with a custom real-time pipeline. Used a ZeroMQ-based communication layer to distribute workloads across multiple processes and achieve real-time performance.
- Built a Dockerized FastAPI pipeline for CAD-to-Unity automation, combining vision-based object matching, image embeddings, and precomputed metadata to automatically map CAD objects to Unity behavior scripts, assign properties, and reduce repeated AI inference calls.
- Created documentation and examples to help technical users understand, configure, and extend the AI automation pipeline.
Eric B.
Last position:
Quality Assurance Manager (QSV) at Federal Employment Agency
Supporting the International Web Presence of the BA (IntWeb) project in the area of quality management, with the aim of taking responsibility for the quality of processes and project results while considering BA standards. The main objectives of the project are to give professionals abroad a quick overview of their opportunities for moving to Germany and to enable them to take the necessary steps consistently and with digital support by providing access to services.
Defining the general guidelines in the QA handbook
Summarising test results in QA reports for PLA
Analysing project results for improvement opportunities
Quality management of requirements analysis (particularly processes, methods, and tools)
Ensuring compliance with SERA requirements
Creating a cross-project test concept
Agreeing on sprint completion reports
Formal review of work results in accordance with requirements and/or the project plan
Contact person for internal audit, external audits by auditors, or BRH (Federal Court of Audit)
Technologies: JIRA, Confluence, MS Office, GitLab, Kubernetes
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.
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Sumalatha B.
Last position:
BC_CDR: Cloud Detection & Response | AI / Cybersecurity at Banyan Cloud
Real-time AWS threat detection using LLMs, UEBA, and vector analytics
- Built UEBA engine processing millions of CloudTrail/GuardDuty events to detect behavioural anomalies in real time.
- Integrated Amazon Bedrock LLMs for narrative threat analysis and structured automated incident summarisation.
- Designed vector embedding pipeline (MongoDB Atlas) for entity behaviour comparison and semantic anomaly detection.
Tech: Python, FastAPI, AWS, MongoDB Atlas, Amazon Bedrock, Pydantic, Motor (Async).
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Prasad T.
Last position:
Solution Architect / Senior Manager – DTC E-Commerce Platform at BRITA
- Led discovery phase and POC for Shopware to Shopify Plus migration across EMEA markets, evaluating platform suitability, technical architecture, and multi-brand/multi-country capabilities against business requirements.
- Designed reference architecture for Shopify Plus implementation incorporating headless front-end patterns (Vue.js, Nuxt.js), CMS integration (Magnolia), and Azure middleware (APIM, Functions, Logic Apps, Service Bus) for 11 EMEA markets.
- Defined migration strategy analyzing data mapping, cutover approach, and zero-downtime deployment patterns using Varnish caching, GitOps pipelines, and CI/CD orchestration across six vendor teams.
- Architected multi-tenant Shopify Plus governance model with centralized admin, localized storefront customization, and compliance controls (GDPR, data residency).
- Prototyped AI-driven search optimization (LLM.txt, JSON-LD) for product discoverability in Google AI results, demonstrating post-launch performance opportunities.
- Defined EMEA expansion roadmap for 15+ markets through C-level strategic workshops, identifying phased rollout, market-specific configurations, and resource requirements.
- Tech Stack: React, Nuxt.js, Vue.js, Magnolia CMS, Shopware, Shopify Plus, Azure (APIM, Functions, Logic Apps, Service Bus, Front Door), Varnish, SAP, MS Dynamics, Docker, Kubernetes, GitHub Actions, PostgreSQL, Kafka
Felix T.
Last position:
Data Engineering and Power BI at Wesernetz Bremen
Independently built a data platform with cross-system process monitoring.
- Designed the Bronze/Silver/Gold architecture and Python-based pipelines for diverse operational sources.
- Cleaned, harmonized, and validated data; processed and stored it in Parquet format.
- Tracked data history and built business-focused fact and dimension models for traceable reporting.
- Automated data delivery with Python and Power Automate (desktop and cloud); used SharePoint for archiving and data access.
- Developed the Power BI solution from data preparation through to visualizing process statuses, work progress, and deviations.
Technologies: Python, SQL, Parquet, Power BI, Power Query, Power Automate, SharePoint, Copilot
Discover over 15,000 top freelancers
Statistics of experts using Apache Spark
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2.7 years
Positions per freelancer
10
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Banking and Finance, Automotive
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
97%
Master's degree or higher
70%
Doctorate
12%
Certifications per freelancer
3
Most common languages
English, German, French
Speak two or more languages
97%
Based on our profile pool as of 5 Nov 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 Apache Spark
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 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Apache Spark 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 (90%)
- Banking and Finance (49%)
- Automotive (41%)
- Professional Services (41%)
- Manufacturing (39%)
- Education (33%)
- Healthcare (31%)
- Retail (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Distributed processing
Apache Spark is an open-source engine for processing large datasets across clusters. It supports batch workloads, interactive analysis, streaming and machine learning through a unified programming model. Teams use it to turn raw data into reliable pipelines, analytical tables and production insights.
Core workloads
Spark handles demanding data tasks where a single machine or a simple database query is not enough:
- Build batch pipelines for structured and semi-structured data
- Process event streams with Structured Streaming
- Transform data for lakehouse and warehouse systems
- Train and score models with MLlib and Spark-based workflows
- Run large-scale SQL analysis with Spark SQL
Ecosystem and tooling
Strong Apache Spark specialists work across Scala, Python and SQL, selecting the right interface for each workload. They use DataFrames, Datasets, RDDs and Spark SQL, then connect Spark with Kafka, Delta Lake, Iceberg, Hive and cloud storage. Familiarity with notebooks, orchestration tools, Git and automated testing supports maintainable delivery.
When expertise matters
Companies bring in freelance Spark expertise when pipelines run slowly, cloud processing costs rise or a prototype must become a dependable production service. Specialists can review partitioning, joins, caching, serialization and cluster configuration. They also help migrate legacy Hadoop or MapReduce workloads and modernize data platforms without disrupting critical reporting.
Delivery in practice
A Spark project often includes ingestion, schema management, transformation logic, quality checks, observability and deployment. Professionals may work with Databricks, Amazon EMR, Google Cloud Dataproc or Azure Synapse, depending on the surrounding platform. Remote collaboration works well when requirements, sample data, access controls and acceptance tests are clearly documented.
What good looks like
Effective Spark professionals understand both distributed systems and the business purpose of the data. They explain why a job is slow, inspect execution plans and design transformations that scale without unnecessary shuffles. Look for practical evidence of resilient pipelines, clear code, tested data contracts, failure handling and measurable improvements in reliability.
Frequently asked questions
Curious about Apache Spark? Here are the answers that come up again and again.
Apache Spark is used for distributed data processing across batch, streaming and interactive workloads. Companies use it for data lake pipelines, large-scale SQL analysis, feature preparation, reporting and machine learning workflows.
Apache Spark provides a broader and more interactive processing model than traditional Hadoop MapReduce. It supports SQL, streaming and machine learning in the same ecosystem, while its execution engine can keep suitable intermediate data in memory and reduce repeated disk work.
Apache Spark is a strong choice when transformations span large or varied datasets, require custom processing or combine batch and streaming inputs. A warehouse may be simpler for governed SQL reporting, while Spark is often useful before data reaches the warehouse or lakehouse.
Apache Spark work usually benefits from skills in Python, Scala, SQL, Kafka, cloud storage and orchestration. Experience with Delta Lake, Apache Iceberg, Hive, Kubernetes, Databricks or cloud cluster services can also matter, depending on the environment.
Apache Spark expertise should match the system risk rather than a fixed career milestone. A contained transformation may need a specialist who can deliver and test a focused pipeline, while a streaming platform or migration needs proven judgment around architecture, performance, security and operations.
Apache Spark projects are often suitable for remote collaboration because code, job definitions and infrastructure can be reviewed asynchronously. Shared documentation, secure development access, representative test data and clear ownership are important when the specialist is not on site.
Apache Spark quality is visible in how a specialist explains execution plans, partitioning, joins, data skew and failure recovery. Ask for examples of production pipelines, testing practices and monitoring, then use a realistic technical review rather than judging only familiarity with APIs.
Apache Spark delivery should include dependable deployment, data quality checks, observability and documentation for ongoing ownership. A strong specialist also leaves clear configuration, sensible cost controls and a plan for handling schema changes, late events and failed jobs.
The average hourly rate of freelancers who have used Apache Spark in their recent projects is 93 €, which corresponds to a daily rate of about 745 € based on an 8-hour working day.
Of the freelancers who have used Apache Spark in their recent projects, 97% hold at least a Bachelor's degree, 70% hold at least a Master's degree, and 12% hold a doctorate.
On average, freelancers who have used Apache Spark in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 2.7 years.
The most common languages among freelancers who have used Apache Spark in their recent projects are English (99%), German (98%), and French (21%).
The most common industries among freelancers who have used Apache Spark in their recent projects are Information Technology (90%), Banking and Finance (49%), and Automotive (41%).
The most common business areas among freelancers who have used Apache Spark in their recent projects are Information Technology (98%), Business Intelligence (85%), and Product Development (81%).
Main locations of FRATCH Experts, who have recently used Apache Spark
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.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Dusseldorf 
Nuremberg 
Vienna 
Zurich Countries:
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