💼 Results-Driven Approach I don’t just develop solutions—I drive business growth with data-driven insights and strategic decision-making. My goal? Maximize efficiency, minimize reporting efforts, and give you the clarity to make high-impact decisions. I’m a certified Power BI, Azure, and Cloud Engineering expert with over 5 years of experience delivering cutting-edge Business Intelligence (BI) and Cloud Engineering solutions for enterprises. With a track record of successful projects, I help businesses transform raw data into actionable insights, streamline reporting, and make high-value business decisions. 🔹 Reduced reporting time by 70-80% 🔹 Optimized business processes with real-time analytics 🔹 Designed enterprise-grade dashboards for finance, real estate, sales, marketing & more 🔑 Why Work With Me? 🎯 Certified & Industry-Recognized Expertise ✅ Microsoft Certified: PL-300 (Power BI), DP-203 (Data Engineering), Azure Developer ✅ Advanced skills in SQL, Python, DAX, M Code, Databricks, Snowflake, AWS, Cloud Computing, and Cloud Engineering. 💡 Custom BI & AI-Powered Solutions 🔹 Power BI & Tableau Dashboards – Interactive, real-time, and tailored for business growth 🔹 Cloud & Data Engineering – Scalable solutions on Azure, AWS, GCP 🔹 AI & Automation – Data-driven automation for efficiency & cost savings 🔹 Embedded Analytics – Seamlessly integrate dashboards into web platforms 📊 What I Deliver ✔ End-to-End BI Solutions (Data modeling, ETL, dashboarding) ✔ Enterprise-Grade Analytics (Finance, Sales, Marketing, HR, Supply Chain, Real Estate, Healthcare) ✔ Cloud & AI Integration (Azure, AWS, OpenAI) ✔ Embedded Analytics – Bring your analytics to your website or application 🚀 Let’s turn your data into a competitive edge. Message me today and let’s get started!

I build robust data infrastructures that empower analytics. With over 6 years of experience in BI and data engineering, I design end-to-end solutions that transform raw data into actionable insights. My expertise includes modeling data warehouses with both star and snowflake schemas, creating reliable ETL/ELT pipelines, and crafting self-service Power BI dashboards that users can trust. Proficient in Snowflake, dbt, and SQL, I excel in adapting to various tech environments, leveraging deep knowledge of the Microsoft ecosystem. I focus on translating complex business requirements into efficient data models, ensuring performance and scalability from inception. If you're looking for a data professional who bridges the gap between technical execution and business needs, let's connect and discuss how I can elevate your analytics capabilities.

I am a 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓 with an extensive, full-stack background in modern data engineering, distributed systems infrastructure, analytics engineering, and business-facing KPI architectures. Over my career, I’ve architected and scaled data platforms for 𝐅𝐢𝐧𝐚𝐧𝐜𝐞, 𝐑𝐞𝐭𝐚𝐢𝐥, 𝐒𝐚𝐚𝐒, and Product teams, managing everything from high-throughput raw streaming telemetry to complex 𝐒𝐡𝐚𝐫𝐞𝐏𝐨𝐢𝐧𝐭 data integrations, enterprise semantic layers, and interactive 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 solutions. Core Engineering & Architecture Stack: • Cloud Warehouses & Lakes: Snowflake, BigQuery, AWS Redshift, Databricks (Delta Lake), PostgreSQL • Integration & Pipelines: Batch & Real-time ETL/ELT, Fivetran, Airbyte, custom API integration frameworks, SharePoint document library and list connectors • Orchestration & Workflow Management: Apache Airflow, Prefect, Dagster • Transformation & Modeling: Advanced dbt (Data Build Tool), SQL, Python (Pandas, PySpark), Data Vault 2.0, Kimball dimensional modeling • Business Intelligence & Visualization: Power BI (Advanced DAX, DirectQuery, Data Modeling), Tableau, Looker, Domo • Metrics Frameworks & Analytics: Centralized KPI definitions, OKR alignment, corporate semantic models • AI & Automation Workflows: Vector databases (Pinecone, Chroma), LLM orchestration pipelines, operational data preparation for ML models • Infrastructure & DevOps: Docker, Terraform (IaC), Git, CI/CD deployment pipelines Highlights: ✔ Engineered fault-tolerant production pipelines processing hundreds of millions of rows daily to feed downstream Power BI environments ✔ Migrated fragmented, on-premise transactional systems and enterprise SharePoint data silos into unified cloud data warehouses ✔ Designed and implemented interactive Power BI KPI dashboards that translated complex raw operational logs into real-time business performance indicators ✔ Automated multi-source data validation workflows, eliminating manual data entry and shifting teams to near-real-time synchronization ✔ Built scalable analytics layers, custom data apps, and semantic models supporting 5000+ interactive dashboard users I specialize in breaking down data silos, defining robust metrics schemas, and building maintainable, high-throughput systems that transform chaotic data landscapes into clean, trusted corporate infrastructure. Let’s architect a data platform your team can scale with confidence! Best, Liam

I am a highly skilled Data Scientist and AI/ML Engineer with expertise in building predictive models, analyzing complex datasets, and delivering data-driven solutions. With 10+ successfully delivered projects in areas like fraud detection, NLP, time series forecasting, and risk analytics, I specialize in turning raw data into actionable insights and scalable AI/ML systems. Key Skills: 📊 Data Science: Exploratory Data Analysis (EDA), Statistical Modeling, Data Visualization, A/B Testing, Hypothesis Testing 🤖 Machine Learning: Predictive Modeling, Time Series Forecasting (ARIMA, LSTM), NLP (Sentiment Analysis, Named Entity Recognition), Deep Learning (CNN, RNN), LLM Fine-Tuning (XLM-Roberta, mBERT), Feature Engineering, Model Explainability (SHAP, LIME) ⚙️ AI Engineering: Model Deployment (Flask, FastAPI, Docker), MLOps (MLFlow, DVC), REST API Development, LLM Deployment and Integration, Cloud Deployment (AWS) 🛠️ Tools: Python, Scikit-learn, TensorFlow, PyTorch, PyMC3, ETL/ELT, PostgreSQL, MySQL, BigQuery, Snowflake, DBT, Pandas, NumPy, YFinance, Matplotlib, Seaborn, Plotly, Streamlit, Dash, Tableau, Power BI, Excel (Advanced), Git/GitHub 🌍 Domain Expertise: Financial Analytics, Healthcare Data, E-Commerce, Water Resources, Energy Markets, Telecom, Insurance, Retail/Pharmaceuticals, Generative AI/NLP When working on a new project, I prioritize clear communication with my clients to fully understand their needs and vision. This ensures that the solutions I deliver are aligned with their goals and expectations. Thank you for considering my profile. I look forward to the opportunity to work with you and contribute to your success!

I have 6 years of experience in Data & Analytics, Business Intelligence and Revenue Growth Management, working across large multinational consumer goods companies (PepsiCo Brazil, PepsiCo Hispanics and Mondelēz International) and technology companies such as Grupo QuintoAndar, one of the largest proptech companies in Latin America, managing over R$100 billion in real estate assets. Currently, I work part-time as a Senior Analytics Engineer building analytics data models, SQL transformations in Databricks that support large-scale business decision making. My work includes data modeling, dimensional modeling (star schema), analytics-ready datasets, business metrics, analytical data pipelines and executive dashboards used across multiple teams. Some times I take freelancers too in the same area. Within the company, I also worked for a short period as a Senior AI Product Analyst, contributing to analytics initiatives related to data-driven product capabilities and AI performance monitoring. That experience led me to my transition to Analytics Engineering. Prior to that, I worked as a Senior Data Analyst, developing complex SQL queries, Python automations in Databricks, analytical data pipelines and advanced analytics frameworks supporting large-scale business decisions. I also have international experience. During university, I participated in a exchange program living in the United States. Later, while working at PepsiCo Latin America, I spent more than a year dedicated to LATAM markets (Dominican Republic, Panama and Haiti), working fully in English and Spanish and traveling extensively across Latin America to collaborate with local teams and commercial partners. More recently, I had the opportunity to experience digital nomadism across Europe, working remotely while living temporarily in several countries. I hold a degree in Electrical Engineering from the Polytechnic School of the University of São Paulo (USP), widely recognized as the top university in Latin America and ranked among the top 100 universities worldwide. I am particularly interested in Data Analytics, Analytics Engineering, Data Engineering, Business Intelligence, Product Analytics and Data Platforms, focusing on building scalable analytical solutions, reliable business metrics and data products that drive real business impact. — Technical stack: SQL, Python Power BI, Looker, Databricks, Dbt, Qlik, Apache Spark, Microsoft Azure, Pandas, Tableau, PostgreSQL, Alteryx, Gsheets.

I’m a Data Engineer with experience building and modernizing data platforms across banking and enterprise environments, with a focus on scalable pipelines, cloud architecture, analytics engineering, and production-grade data systems. My foundation in data started from academic work in data management, systems analysis, information systems design, and research methodologies. During my thesis, I explored data mining techniques to analyze the relationship between hard work and quality of life, covering data collection, cleaning, preprocessing, feature engineering, predictive modeling, statistical analysis, correlation testing, and model evaluation. This background shaped how I approach data work today: with structure, validation, and a focus on turning raw information into reliable insight. Professionally, I have worked on large-scale data initiatives that combine engineering execution with business impact. I led data engineering work for a national-scale migration initiative at Astra Honda Motor, supporting the transition from a legacy monolithic system to a modern microservice-based architecture across dealer networks in Indonesia. This involved designing transformation pipelines, validating master data and outstanding transactions, coordinating strict migration windows, resolving critical data issues, and ensuring smooth post-migration operations. I also led analytical reporting and platform modernization initiatives at Astra International, where I supported the transition from legacy reporting workloads to modern analytics platforms such as Azure Synapse and Microsoft Fabric. My work covered reporting development, dbt and PySpark-based transformations, Silver-layer curated datasets, outbound report validation, Excel exports, SharePoint delivery, and text file outputs for downstream systems. Currently, my work focuses on maintaining and enhancing end-to-end data pipelines from core banking systems, transactional platforms, third-party sources, and internal business tools. I work across batch and real-time architectures to ensure data availability, reliability, and accuracy for analytics, reporting, and decision-making. My technical experience includes Google Cloud services such as BigQuery, Dataproc, Cloud Storage, Cloud Functions, and Google Cloud Composer with Apache Airflow for orchestration. I also support real-time Change Data Capture pipelines using Confluent Cloud, Apache Kafka, and Debezium. In analytics engineering, I use dbt to build scalable data models, improve lineage, apply testing frameworks, and increase confidence in reporting outputs. I enjoy solving complex data problems, simplifying messy processes, improving platform reliability, and building systems that stakeholders can depend on. My goal is to bridge technical depth with business value by delivering data solutions that are accurate, scalable, maintainable, and ready for real operational use.

Data Engineer and Software Engineer with 4+ years of professional experience building reliable data and software solutions. I help businesses collect, process, and leverage their data by designing automated data pipelines, ETL workflows, analytics platforms, and backend services. My recent projects include building end-to-end data platforms using Python, PostgreSQL, dbt, Apache Airflow, FastAPI, Docker, and cloud-based technologies. My background in software engineering and scientific computing allows me to approach complex technical challenges with rigor, scalability, and attention to detail. Whether it's developing data pipelines, designing APIs, automating workflows, or building custom Python applications, I focus on delivering maintainable solutions that create real business value. My core skills include: • Data Engineering (ETL/ELT, Data Pipelines, Data Modeling, PostgreSQL, dbt, Apache Airflow) • Python Development (Backend Services, APIs, Automation, Data Processing) • Cloud & DevOps (Docker, AWS, CI/CD) • Analytics & Reporting (SQL, Streamlit, Dashboards) Clear communication, understanding business requirements, and delivering high-quality work are at the center of every project I take on.

As a Cloudera Certified Data Engineer, I specialize in designing, building, and optimizing large-scale data infrastructures. My ultimate goal is to ensure your data is highly accurate, accessible, and ready for high-performance analytics. I believe that data silos and fragile pipelines are the biggest bottlenecks to business growth. My primary focus is to help you transform raw data from multiple sources into clean, structured, and actionable insights that empower your analysts and data science teams. My Technical Expertise & Services: - Modern Data Orchestration: Designing and scheduling complex, automated ETL/ELT workflows using Apache Airflow. - Data Transformation & Modeling: Building scalable data models and ensuring warehouse-level data quality using dbt (data build tool). - Big Data Processing: Efficiently handling massive data volumes using the Hadoop ecosystem and Apache Spark. - Real-time Data Streaming: Architecting low-latency streaming pipelines using Apache Kafka for real-time analytics. My Cloudera Data Engineering certification guarantees that my work meets enterprise-level standards. I don't just write code—I build scalable, cost-efficient, and maintainable data architectures. I am highly experienced in bridging the gap between legacy data systems and the Modern Data Stack. Whether you are a fast-paced startup building your data foundation from scratch or an enterprise looking to optimize your existing pipelines, I am here to deliver robust solutions. Let's discuss your project! Send me a message or click "Invite to Job" at the top right, and let's architect the perfect data solution for your business.

Turning messy data into decisions clients can actually act on — that's what I've spent the last 6+ years doing for healthcare, insurance, e-commerce, and SMB clients across cloud, BI, and machine learning projects. If you have data sitting in spreadsheets, a warehouse, or scattered across tools and you're not sure what it's telling you — I can help. I build the full stack: clean pipelines, predictive models, and dashboards your team will actually open every morning. What I deliver: Exploratory and statistical data analysis that surfaces real patterns, not vanity metrics Predictive models (churn, forecasting, classification, risk scoring) built with scikit-learn, XGBoost, and PyTorch Interactive BI dashboards in Power BI, Tableau, and Looker End-to-end ML pipelines on AWS, Azure, GCP, and Databricks Data engineering with Python, SQL, dbt, and modern warehouses (Snowflake, BigQuery, Redshift) Industries I know well: Healthcare (HIPAA-aware workflows, claims data, patient analytics), insurance (underwriting models, risk analytics, policy data), e-commerce (customer segmentation, LTV, demand forecasting), and SMBs/startups looking to build their first real data function. How I work: Quick discovery call → scoped proposal with milestones → weekly demos → clean handoff with documentation. No black boxes, no "trust me it works." You see the work as it's built. Let's chat about what you're trying to figure out from your data. ------------------------------------------------------------------------------------------------------------------------ Service Packages 📊 Package 1 — Cloud Data Analysis & Insights Starting at $800 Have data in the cloud (Snowflake, BigQuery, Redshift, S3, Azure) but can't see clearly what's happening in your business? This package transforms your existing cloud data into actionable insights. You get one of the following: Deep exploratory data analysis with a written insights report A predictive model (forecasting, classification, or segmentation) with documented results An interactive BI dashboard (Power BI / Tableau / Looker) connected to your cloud source Best for: Companies with data already in the cloud who need a focused, single-deliverable engagement. Typical turnaround: 7–10 days ⚡ Package 2 — Enhanced Data Solutions Starting at $1,400 For businesses ready to combine multiple data services into one cohesive solution. Whether your data lives in Excel, Google Sheets, or the cloud, I'll integrate two or three deliverables into a unified workflow. Choose any two or three: Full data analysis with insights documentation Predictive modeling with deployed scoring logic Interactive dashboard with scheduled refreshes Best for: Teams who need both the analysis and the visualization, or analysis and a model — delivered as one integrated project. Typical turnaround: 2–3 weeks 🚀 Package 3 — Complete ML Pipeline & Dashboard Starting at $3,500 End-to-end machine learning solution. From raw data ingestion (email, APIs, file drops) through cloud storage, model training, and a live dashboard your team uses daily. Includes: Data ingestion pipeline (email parsing, API connectors, scheduled imports) Cloud storage architecture and ETL (AWS / Azure / GCP / Databricks) Model development, training, and deployment Real-time dashboard with automated refreshes Documentation, handoff training, and 30 days of post-launch support Best for: Companies ready to operationalize machine learning and replace manual reporting with a real data product. Typical turnaround: 4–6 weeks

𝘽𝙖𝙙 𝙉𝙇2𝙎𝙌𝙇 𝙞𝙨 𝙡𝙞𝙠𝙚 𝙖 𝙂𝙋𝙎 𝙩𝙝𝙖𝙩 𝙘𝙤𝙣𝙛𝙞𝙙𝙚𝙣𝙩𝙡𝙮 𝙙𝙧𝙞𝙫𝙚𝙨 𝙮𝙤𝙪𝙧 𝙢𝙚𝙩𝙧𝙞𝙘𝙨 𝙞𝙣𝙩𝙤 𝙖 𝙡𝙖𝙠𝙚. I help teams build AI data agents that turn plain-English questions into trusted answers from live business data. My focus is not just generating SQL. I build the data, semantic, retrieval, and evaluation layers that make AI analytics reliable enough for real business use. I work with startups, SaaS teams, analytics platforms, and data-heavy products that want to build AI analysts, data copilots, NL2SQL tools, or MCP-powered warehouse agents. What I can help with: - AI data agents - NL2SQL and text-to-SQL systems - Semantic layers and certified metrics - Metadata RAG over schemas, tables, columns, and glossary terms - MCP-based database and warehouse tools - SQL validation and read-only query workflows - Golden datasets, regression tests, and answer-quality evals - Data quality checks, freshness validation, and schema drift detection - Python backend services for AI and data workflows My approach is simple: the AI should never guess what the business means. For metrics like revenue, churn, occupancy, active users, conversion, bookings, or retention, I help define clear metric rules, trusted tables, safe join paths, and reusable business context. This helps the agent return the same correct answer instead of changing the SQL every time the question is worded differently. I also build retrieval pipelines that help the model choose the right context. That includes schema documentation, glossary terms, verified SQL examples, embeddings, vector search, and filtering out irrelevant tables before the LLM generates SQL. Tools I commonly work with include Python, SQL, FastAPI, PostgreSQL, BigQuery, Snowflake, pgvector, LangChain, LlamaIndex, dbt-style models, MCP tools, and cloud platforms like GCP, AWS, and Azure. I care about evaluation from the start. A confident wrong number is worse than no answer, so I use golden datasets, reconciliation checks, SQL comparison, regression testing, and root-cause analysis when an AI-generated number does not match trusted reports. I do not treat AI analytics as a chatbot project. I treat it as a production data trust problem. If you are building an AI analyst, semantic data agent, NL2SQL platform, MCP warehouse tool, or internal data copilot, I can help make it reliable, explainable, and ready for real users. Message me and I’ll help you map the fastest path from idea to launch. 📃 Keywords AI Engineer | AI Data Agent Engineer | NL2SQL Engineer | Text-to-SQL Developer | AI Analytics Engineer | Semantic Layer Engineer | Python Developer | SQL Expert | Data Engineering | LLM Developer | MCP Developer | Model Context Protocol | LangChain | LlamaIndex | Vector Database | Metadata RAG | pgvector | BigQuery | Snowflake | PostgreSQL | Data Quality Testing | Regression Testing | AI Evaluation Systems