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𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘄𝗶𝘁𝗵 𝟰+ 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. I design and implement 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 data and BI solutions for clients across banking, energy, hospitality, retail, public sector and marketing. ✔️ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗮𝗯𝗿𝗶𝗰 𝗮𝗻𝗱 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 Tenant and workspace governance, OneLake security, capacity management, semantic modeling, DAX optimization, CI/CD with Azure DevOps. ✔️ 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 Lakehouse architecture, PySpark ETL/ELT pipelines, Delta Lake, Unity Catalog, medallion architecture, data quality frameworks. ✔️ 𝗦𝘁𝗮𝗰𝗸 SQL, Python, Apache Spark, Power BI, Excel. Microsoft Fabric, Databricks, Azure, AWS, Snowflake. ✔️ 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 Microsoft PL-300 (Power BI Data Analyst), DP-600 (Fabric Analytics Engineer), DP-700 (Fabric Data Engineer), Databricks Certified Data Engineer Professional. Async-first delivery for clients in Spain, Switzerland, Germany, UK and the US.

Data Engineer currently working for banking with great knowledge of the Azure cloud platform and experience in Big Data Technologies.

I help companies turn messy, scattered data into reliable systems that power real decisions, from cloud data warehouses processing hundreds of millions of daily events for millions of users, to multi-terabyte tables with tens of billions of records that I redesign and optimize to cut load times and costs by up to 60%. With 6+ years across startups, SMBs, and global companies, I build the full data stack end to end: scalable pipelines and data warehouses, workflow automation, and the dashboards and analytics that sit on top. Many of my clients come to me for "a dashboard" or "help with their data" — and what they actually need is the right data foundation underneath. I build both. 🔑 Challenges I Help Solve 📉 Scattered, inconsistent data: Information spread across multiple tools, making accurate reporting and decisions hard. ⏰ Manual, repetitive work: Hours lost on tasks and reports that should be automated. 🌌 Data but no insight: Plenty of data, but no clear answers to act on. ⚙️ Slow or costly pipelines: Infrastructure that breaks, lags, or scales badly. ⚙️ What I Do - Data Engineering & Warehousing: Design, build, and redesign data warehouses and lakehouses (BigQuery, Databricks, Snowflake) that handle massive, high-volume workloads. - ETL & Automation: Build scalable pipelines and automate workflows and reporting with Airflow, Python, Spark, SQL, and Apps Script. Clean, reliable, on time. - Cloud: Deploy and manage solutions on Google Cloud (GCP), AWS, and Azure. - Analytics & Dashboards: Turn that data into clear, decision-ready dashboards in Tableau, Looker Studio, Power BI, and Metabase. - AI & Advanced Analytics: Apply data science and ML to detect trends, forecast, and build intelligent, data-driven applications. 🛠️ My Toolkit Languages & Engineering: SQL, Python, R, Spark, Airflow, Databricks, dbt, Apps Script Cloud: Google Cloud (BigQuery, Cloud Run, Pub/Sub), AWS, Azure Analytics & Visualization: Tableau, Looker Studio, Power BI, Metabase, Microstrategy, Google Sheets / Excel AI & Automation: Generative AI, workflow automation, data-driven applications 💼 Proven Results - Designed and optimized cloud data warehouses processing hundreds of millions of daily events for millions of users. - Redesigned data architectures with multi-terabyte tables and tens of billions of records, cutting load times and infrastructure costs by up to 60%. - Delivered ETL, automation, and dashboards for 30+ businesses worldwide. - Developed AI-powered solutions such as a RAG-based chatbot and predictive models. 📈 Whether you need to set up your first data warehouse, automate a painful manual process, optimize slow pipelines, or finally get the dashboards your team can trust, I can design the right solution for your business. Let's connect and discuss what you're trying to achieve.

Many AI projects fail not because of the algorithms, but because the underlying scientific or engineering problem is misunderstood. My background allows me to bridge that gap. I hold a PhD in chemistry and technology and have many years of experience working on research, industrial processes, data analysis, and software development. My background includes patents, peer-reviewed publications, and practical industrial experience, allowing me to approach projects from both scientific and engineering perspectives. Over time, my work naturally expanded into machine learning and AI, where I now build solutions for both scientific and commercial applications. I work with clients who need more than someone who can train another neural network. Often the real challenge is understanding noisy experimental data, choosing the right model, designing a reliable workflow, or translating domain knowledge into software. Typical projects include: • Machine learning and deep learning • Large language model applications and AI agents • Scientific data analysis and visualization • Chemometrics and multivariate statistics • Computational chemistry • Spectroscopy, chromatography and mass spectrometry data • Process optimization and modeling • Python software for research and engineering • Custom algorithms and automation I choose technologies based on the problem rather than the other way around. My primary tools include Python (NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, Keras), R, MATLAB, and modern cloud technologies whenever they make sense for the project. I enjoy difficult problems, especially when they sit at the intersection of science and software. Some clients come to me with a well-defined specification; others only know that "the data doesn't make sense yet." Both are perfectly fine. Whether you represent a startup exploring a new idea, an established company improving an existing process, or a research group working with complex experimental data, I'll be happy to discuss your project and see whether I'm the right person to help.

I help companies turn complex, messy data into reliable, production-ready systems for analytics, reporting, and machine learning. I am a Senior Data Engineer and Cloud Engineer with over 8 years of experience building end-to-end data platforms, large-scale ETL pipelines, and cloud-native architectures on AWS and Azure. Most of my clients come to me when their pipelines are slow, expensive, fragile, or difficult to maintain. I design and rebuild data systems so they can run daily without breaking, handle growing volumes, and support real business decisions. I work across the full data lifecycle: ingestion, transformation, modeling, quality checks, and delivery. I build pipelines that pull data from APIs, databases, files, and event streams, transform it with Spark and SQL, and store it in data lakes and warehouses that power dashboards, analytics, and machine learning. I am AWS Certified and have built multiple production platforms using S3, Glue, EMR, Iceberg, Athena, Redshift, Lambda, IAM, and CloudFormation. I also have experience with Azure Data Factory, Databricks, and ADLS, as well as CI/CD tools such as Jenkins and GitHub Actions to keep data pipelines reliable and deployable. A strong part of my expertise is Spark and PySpark optimization. I regularly improve slow or unstable pipelines using techniques such as partitioning strategies, shuffle reduction, broadcast joins, caching, and breaking long DAGs with checkpoints. I have built highly optimized Spark pipelines processing millions of records daily in a stable and cost-efficient way. I have also implemented plug-and-play ETL frameworks that allow multiple teams to ingest and transform data from a shared repository, as well as data quality and ML monitoring using tools like Evidently to track drift and model performance. For business users and data science teams, I have delivered dashboards and reporting systems using Power BI and Amazon QuickSight. In addition to data engineering, I also have experience building web and mobile applications with React, which allows me to design data systems that integrate cleanly with real products and user interfaces. If you need someone who can not only write code but design, optimize, and operate serious data systems in production, I would be happy to help.

Senior Data Engineer & BI Architect | Power BI, Snowflake, Data Platforms | Founder @ Datavision 🚀 Senior Data Engineer & BI Architect I operate under my consulting brand Datavision, focused on building scalable data platforms and high-performance BI solutions. I help companies transform complex data into scalable, production-ready analytics platforms. With 12+ years of experience across retail, industrial, and enterprise environments, I design and optimize modern data stacks using: • Snowflake • Databricks • dbt (modular & incremental modeling) • Airflow (orchestration) • Azure Data Factory • Power BI & Semantic Models 🔹 I specialize in medallion architectures (Raw → Silver → Gold) 🔹 I build production-grade ETL/ELT pipelines 🔹 I optimize performance & cost in cloud data platforms 🔹 I implement CI/CD for analytics workflows Recently worked with large European companies delivering scalable cloud data platforms and business-critical dashboards. If you need: ✔ A clean, scalable data architecture ✔ Optimization of slow pipelines ✔ A robust BI layer built properly ✔ Governance & best practices Let’s build it the right way.