Data Engineering | ETL | Data Quality | Data Modeling | Data Pipelines | Big Data | Data Transformation | Reporting | Automation | Python | SQL | Azure | Databricks | Pipeline Orchestration With over 3 years of experience in Data & Analytics, I have worked as a Senior Consultant at a Big 4 firm, delivering high-impact solutions for clients in various industries. I am now seeking to transition into freelancing to build a solid outsourcing portfolio. 💻 My technical expertise includes delivering end-to-end data engineering solutions, encompassing data pipeline development, performance optimization, data orchestration, data ingestion from multiple data systems, data quality management, scalable data modeling, data governance, process automation and reporting. As a consultant, I am also highly proficient in translating business requirements into technical specifications, ensuring that solutions align with client needs and objectives. I have extensive experience working with Azure and am certified as an Azure Data Engineer Associate (DP-203) and Azure AI Engineer Associate (AI-102). 📚 I hold a Master’s Degree in Aerospace Engineering from Portugal's top-ranked engineering university, where I excelled as one of the top students, entering with a remarkable average of 195 out of 200. Experienced in: ✅ Python, SQL, Spark ✅ Databricks ✅ Azure Data Factory, Azure Synapse ✅ Airflow ✅ DBT ✅ Azure Cloud ✅ PostgreSQL, MySQL ✅ Power BI, Tableau ✅ Azure DevOps ✅ Git ✅ Docker ✅ Agile development

Are you struggling to turn raw data into clear, actionable insights? I help businesses build reliable, scalable data pipelines and analytics platforms — so you spend less time wrestling with data and more time making decisions. I’m a Data & Analytics Engineer with hands-on experience delivering end-to-end data solutions using Azure , Snowflake and Databricks. From ingestion to visualization, I build systems that are efficient, maintainable, and business-focused. What I Can Help You With? - Building ETL/ELT pipelines - Architecting cloud data warehouses - Real-time & batch data ingestion - Dimensional modeling for analytics-ready data marts - Creating insightful dashboards with Power BI What You Get - Clean, well-structured data you can trust - Scalable solutions that grow with your business - Faster access to insights and better decision-making - Clear communication and reliable delivery

Seasoned Data Engineer & Backend Developer with deep expertise in building high-performance, secure, and scalable data platforms for industries like Fintech, Health & Nutrition Tech, Digital Media (Amazon Music), Insurance, and Gaming/Sports Betting. I specialize in Python-driven data architectures on AWS and Azure, enabling real-time analytics, rigorous data quality, and secure data handling for batch and streaming workloads. My solutions turn complex datasets into business intelligence that powers product innovation and regulatory compliance. 🔹 Cloud-Native Data Pipelines – Expert in architecting ELT/ETL workflows using Apache Airflow, DBT, AWS Glue, and Azure Data Factory. 🔹 Streaming & Real-Time Systems – Built Kafka- and Kinesis-based pipelines with schema registry, Spark Streaming, and Azure Event Hubs. 🔹 Secure & Governed Architecture – Implemented granular IAM, end-to-end encryption, metadata lineage (AWS Glue Catalog, Azure Purview), and compliance controls for SOC and HIPAA-style standards. 🔹 Backend & Microservices – Designed modular FastAPI/Flask services, asynchronous job queues (Celery, Lambda), and robust APIs (OAuth2, JWT, API Gateway). 🔹 Data Modeling & Storage Optimization – Skilled in dimensional models, data vaults, and tuning storage engines (PostgreSQL, Redshift, Snowflake, S3/ADLS Lakehouses). 🎧 On Amazon Music, I contributed to scalable event-driven services and analytics pipelines that processed billions of user interactions to drive personalization and reporting. ✅ Let’s work together if you need: Real-time & batch data infrastructure Secure cloud data platforms (AWS, Azure) Streaming data ingestion & CDC (Debezium, Kafka) Optimized data lake/warehouse design Backend services to support analytics and product features 📈 Whether you're building a modern data stack or re-architecting legacy systems, I bring hands-on expertise, architectural clarity, and a delivery mindset.

I am a Snowflake and dbt focused Data Engineer with 5+ years of experience building scalable, analytics-ready data warehouses. I specialize in Snowflake ELT pipelines, dbt transformations, and Azure-based data ingestion, with a strong focus on performance, data quality, and cost optimization. My expertise includes Snowflake data warehouse design, dbt modeling and testing, incremental data loads, and Azure Data Factory pipeline development. I have built production-grade systems that reliably process large datasets while ensuring clean, consistent data for analytics and reporting. Core skills include Snowflake architecture and performance tuning, dbt transformations and documentation, Azure Data Factory pipeline design, Python and SQL for data processing and validation, and data quality monitoring. Selected projects include: Azure Data Factory to Snowflake ELT Pipeline Designed and implemented parameterized Azure Data Factory pipelines to ingest data from multiple sources into Snowflake. Built retry mechanisms, monitoring, and error handling for production reliability. Impact: Reduced manual ingestion effort by approximately 70% and improved pipeline success rates to over 99%. Snowflake Data Warehouse Optimization Redesigned schemas, optimized warehouse sizing, and improved query patterns to support analytical workloads at scale. Impact: Reduced average query execution time by around 35% and lowered Snowflake compute costs by approximately 25%. dbt Analytics Engineering Framework Built a structured dbt project with staging, intermediate, and mart layers, including tests and documentation. Implemented incremental models for large fact tables. Impact: Improved data consistency and reduced data-related production issues by roughly 40%. Automated Data Quality and Reconciliation Framework Developed Python and SQL-based validation checks for schema accuracy, row counts, and cross-system reconciliation within Snowflake. Impact: Achieved 99.9% data accuracy and significantly reduced downstream reporting errors. I take full ownership of data pipelines from ingestion to analytics delivery and focus on clean, maintainable solutions. If you are looking for a Snowflake and dbt specialist who can design, optimize, and scale your data warehouse, I am ready to help.

A failing data pipeline is usually not the complete problem. The deeper issue may be poor architecture, inconsistent source data, missing validation, weak orchestration, slow SQL models, uncontrolled cloud costs, or a platform that was never designed to scale. I help companies identify the real constraint and build a data system that is reliable, observable, scalable, and useful to the business. With 8+ years of experience across data engineering and cloud infrastructure, I support organizations that need to: 📉 Eliminate recurring pipeline failures ⏱️ Reduce reporting delays and manual processing 🔗 Integrate disconnected applications, APIs, and databases 📊 Create trustworthy datasets for dashboards and analytics ☁️ Modernize legacy infrastructure in AWS, GCP, or Azure 🤖 Prepare structured data for machine learning and AI 💰 Improve performance without wasting cloud resources 🚀 How I Solve Data Problems 🔎 Discovery and Diagnosis I review your existing architecture, data sources, pipelines, reporting requirements, failure points, processing volumes, and business priorities. The objective is to determine whether the real bottleneck is ingestion, transformation, modeling, orchestration, infrastructure, data quality, or downstream reporting. 🏗️ Architecture and Implementation Based on the diagnosis, I design a practical solution that may include: * Batch or real-time ingestion pipelines * Cloud data lakes, warehouses, or lakehouses * ETL/ELT orchestration and automation * Dimensional and analytics-ready data models * Data validation and quality controls * Monitoring, alerts, retries, and failure recovery * Infrastructure as Code and CI/CD * Performance and cost optimization 🛡️ Stabilization and Accountability Delivery does not end when the code runs once. I focus on production readiness through documentation, testing, logging, monitoring, deployment processes, ownership clarity, and maintainable architecture. 🛠️ Technical Expertise Python | SQL | PySpark | Apache Airflow | Apache Spark | Kafka | dbt | Databricks | AWS Glue | Google Dataflow | BigQuery | Snowflake | Redshift | Azure Synapse | Docker | Kubernetes | Terraform | GitHub Actions | Power BI | Tableau | MLflow 💼 What You Receive ✅ A clear understanding of the root problem ✅ An architecture aligned with your actual requirements ✅ Reliable and maintainable production pipelines ✅ Clean, validated, analytics-ready datasets ✅ Monitoring and visibility into pipeline health ✅ Documentation your internal team can understand ✅ A scalable foundation for reporting, automation, and AI I work as a technical partner not an order taker. Instead of simply implementing the first tool requested, I help determine what should be built, why it should be built, and how it will improve reliability, speed, cost, or decision-making. 📩 Share your current challenge, architecture, and expected outcome. I’ll help you identify the most important issue to solve first.

I build data systems and AI-powered products that actually ship. With 5+ years across data engineering, analytics, and ML — and a recent pivot into full-stack AI development — I work at the intersection of data infrastructure and intelligent product design. My background spans AdTech (WPP/Choreograph, Coca-Cola campaigns), sports betting, GIS simulation, and independent AI app development. What I help clients with: 📊 Data Engineering & Analytics – Data modelling and transformation pipelines (dbt, SQL, BigQuery, AWS) – End-to-end analytics stacks built from scratch (GA4, Firebase, Appsflyer, Amplitude) – Data quality systems: QA strategy, dbt tests, monitoring dashboards 🤖 AI & LLM Integration – LLM automation pipelines: content generation, translation, multi-platform publishing – AI-powered product development (OpenAI API, LangChain, CrewAI, n8n) – Rapid MVP prototyping with AI-assisted development (Cursor, Copilot) 🗺️ GIS & Spatial Analytics – Geospatial data modelling (H3 grid, Kepler.gl, Carto, QGIS) – Spatial ETL pipelines, visualisation, and interactive map-based apps 📈 Product & Marketing Analytics – Web/app analytics setup (GTM, GA4, Firebase, Mixpanel) – A/B testing, funnel analysis, cohort dashboards – Attribution modelling, campaign performance analysis Recent highlights: ✅ Built a full-stack GIS simulation app solo in weeks (dbt pipeline + backend + LLM features + map UI) ✅ Delivered an AI art-to-product app: photo → wall art → 3D viewer → video → physical product ✅ Built an LLM pipeline automating multilingual content publishing across web + social ✅ Led a team of 5 data scientists on a privacy-first AdTech platform (100+ dbt models) I write clean, documented, production-ready code. I move fast — and I don't disappear after delivery.

🚀 Data Engineer & Scientist (E-commerce) | Data Pipelines & Warehousing | GCP | BigQuery | dbt | SQL | Analytics & ML-ready Data | Visualization | A/B Testing Reliable data foundations that actually drive decisions. I’m a data engineer with 6+ years of experience building end-to-end data pipelines and centralized data warehouses for e-commerce businesses, with a strong analytics and data science background. My work sits at the intersection of data engineering and decision-making—I don’t just move data, I make sure it can be trusted, queried, visualized, and used to guide strategy. I work from first principles. Every project starts by understanding the final context: what questions the business needs to answer, which teams will use the data, and how the data supports analytics, experimentation, or machine learning. From there, I design the warehouse, tables, and pipelines to serve those goals—not just to collect data. 🔍 What I Build for You ✅ Centralized E-commerce Data Warehouses I ingest and model data from Shopify, GA4, Meta Ads, Google Ads, CRM, and websites into BigQuery and similar cloud data warehouses (Snowflake, Redshift, Databricks), ensuring clean schemas and joinable entities across products, orders, customers, and marketing performance. ✅ Production-Ready Data Pipelines I define update frequency, timestamp logic, and data volume expectations, then implement scalable pipelines using dbt and orchestration best practices—built for reliability, freshness, and long-term use. ✅ Analytics- & ML-Ready Data Models I design tables with downstream analytics and modeling in mind, so BI, experimentation, and machine learning workflows don’t break as requirements evolve. ✅ Data Validation, Freshness & Trust I validate warehouse data against source systems, establish sanity and freshness checks, and make sure teams can confidently rely on the numbers. ✅ Experiment Analysis That Drives Confident Decisions I help you run smarter experiments—modeling uplift, revenue impact, and statistical certainty—so you can ship with confidence, not guesswork. ✅ Seamless Analytics Stack Migrations for Your Peace of Mind Migrating tracking or reporting systems? I’ve got you covered—from schema restructuring to recreating critical reports and cohorts—without losing continuity. 📊 From Warehouse to Business Insight (Data Science & Analytics) Once the data foundation is solid, I help teams translate it into clear, value-driven dashboards and analyses—highlighting metrics that actually matter (revenue quality, conversion drivers, retention, ROAS). This ensures the warehouse doesn’t become shelfware, but a tool that actively informs product, marketing, and growth strategy. I’ve supported multiple high-growth e-commerce brands, partnering closely with founders, product teams, and engineers to build data systems that scale—and insights that lead to action. Let’s build data infrastructure that your business can trust and actually use. 💡📈 ⸻ 🛠️ Tech Stack 🧱 Data Engineering & Warehousing (Primary Focus) BigQuery, Snowflake, PostgreSQL, MongoDB, AWS Redshift dbt, Airflow, ETL / ELT Design, Data Modeling, Schema Design Google Cloud Platform (GCP), AWS, Azure, Docker 🗣️ Programming & Querying SQL, Python Data Modeling, Transformation Logic 📊 Analytics, Visualization & Experimentation Looker Studio, Looker, Tableau, Power BI Metric Design, Funnel Analysis, Experiment Readouts 🛒 E-commerce & Marketing Platforms Shopify, GA4, Meta Ads, Google Ads, Web & App Event Tracking 🤝 Ways of Working Clear milestones, scoped deliverables, and clean handovers Strong collaboration with founders, PMs, and engineers

Hi! I'm a Data Engineer based in Belgrade, currently pursuing a Master's in Information Engineering. Over the past 2 years, I've been building and maintaining cloud-based data pipelines, data models, and analytics workflows, mainly using Python, SQL, Spark, BigQuery, Databricks, and Infrastructure as Code with Terraform. What I can help you with: - Building and maintaining ETL/ELT pipelines using Python, SQL, and Spark - BigQuery + Dataform transformations, data modeling, and query optimization - Workflow orchestration with tools such as Dagster and Airflow-style patterns - Setting up and improving cloud infrastructure with Terraform - Working with AWS and GCP services for data ingestion, storage, and processing - Improving pipeline reliability, data quality, monitoring, and maintainability - Organizing raw data into clean, analytics-ready tables and models I've worked on projects involving e-commerce supplier data integration, cloud pipeline development, BigQuery model refactoring, event-driven AWS ingestion, and Spark/Delta Lake analytics platforms. I enjoy taking messy or unstructured data workflows and turning them into systems that are easier to understand, operate, and scale. If you tell me your goal - what data you have, where it should land, how often it should run, and what you want to analyze - I'll suggest a practical plan and we can start with a small first milestone.

Data Analyst with experience in analysing business performance and operational metrics across digital products and web-based services. Skilled in SQL, Python, and dbt for building analytics-ready datasets. Experienced in statistical analysis, basic machine learning, KPI design, and behavioural segmentation to identify trends, support decision-making, and improve business performance. 𝐈’𝐦 𝐩𝐚𝐫𝐭𝐢𝐜𝐮𝐥𝐚𝐫𝐥𝐲 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐫𝐨𝐥𝐞𝐬 𝐰𝐡𝐞𝐫𝐞 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐮𝐬𝐞𝐝 𝐭𝐨: • Improve service delivery, onboarding, and retention; • Understand user behaviour and operational workflows; • Build reliable data models; and • Support product, operations, and growth decisions. If your team works with behavioural data, digital products, web-based services, or related fields, I'd be glad to connect.

AI & Data Engineer Sometimes a business has the perfect product, the perfect team, and the perfect strategy — but still struggles. Not because what they offer is wrong… but because their data isn’t structured, optimized, or automated in a way that supports growth I’ve seen companies sitting on massive value while drowning in messy spreadsheets, broken pipelines, inconsistent KPIs, slow dashboards, or unreliable reporting. The problem isn’t what they’re selling — it’s how their data is being captured, processed, and transformed That’s where I come in As an AI-Driven Data Engineer & Analytics Specialist with 2+ years of experience, I help businesses turn raw, chaotic data into clean, automated, AI-powered systems that drive real decisions, real efficiency, and real revenue Great data infrastructure doesn’t just look good in a dashboard 👉 it grabs attention, powers insights, and drives smarter, faster business growth 👉 WHAT I OFFER (AI, Data Engineering & Analytics) ⚡ ETL/ELT Pipelines – Automated, scalable pipelines using SSIS, Airflow, Airbyte, Python & SQL. ⚡ Data Warehousing – STG/ODS/DWH design using SQL Server, Snowflake, ClickHouse, and Azure Synapse ⚡ Real-Time Data Streaming – Kafka & Spark pipelines for fast, reliable event processing ⚡ Business Intelligence Dashboards – Power BI & Tableau dashboards that turn data into decisions ⚡ Data Modeling (OLTP → OLAP) – Star/Snowflake schemas that optimize analytics performance ⚡ Cloud Data Engineering – Azure Data Factory, Databricks, Snowflake, dbt & modern MDS architectures ⚡ AI-Assisted Automation – Data validation, anomaly detection, SQL generation, documentation & workflow optimization ⚡ Data Quality Frameworks – Automated testing, monitoring & CI/CD-driven data workflows 👉 EXTENSIVE EXPERIENCE IN THE FOLLOWING ⚡ AI Data Workflows – Integrating LLMs for faster validation, optimization & documentation ⚡ DataOps Practices – CI/CD, version control, observability & automated QA for data pipelines ⚡ Modern Data Stack – Airflow, Airbyte, dbt, ClickHouse, Snowflake, Databricks, Synapse ⚡ Big Data Processing – Spark, PySpark, Kafka streaming, and distributed compute ⚡ Visual Analytics – KPI modeling, dashboard design, and analytical storytelling ⚡ Cloud Architecture – Azure ecosystem (ADF, ADLS, Synapse, Databricks) ⚡ End-to-End Data Solutions – From ingestion → transformation → modeling → analytics → automation ✨ TOOLS I USE ⚡ Python & SQL – Core engines for transformation, validation & analytics ⚡ Airflow – Orchestration of complex workflows ⚡ Airbyte – Connector-based ingestion from multiple sources ⚡ dbt – ELT modeling, testing, documentation & lineage ⚡ Azure Data Factory – Cloud orchestration and pipelines at scale ⚡ Databricks (PySpark/Delta) – High-performance data processing ⚡ SQL Server / SSIS / SSAS – Enterprise-grade warehousing and OLAP analytics ⚡ Power BI & Tableau – Turning data into high-value insights ⚡ ClickHouse – Ultra-fast analytical storage ⚡ Git & Azure DevOps – CI/CD, automation, and data versioning 🎯 Final Message Your business doesn’t just need dashboards — It needs data systems that work, pipelines that never break, models that scale, and AI-driven workflows that save hours and multiply output If you’re ready to build powerful, automated, and intelligent data infrastructure that increases performance, accuracy, and revenue… 📩 Send me a message and let’s build something extraordinary