Skills and Expertise: ✅ Proficient in writing complex queries and optimizing database (SQL and NoSQL) performance. ✅ Experienced in Programming Languages such as Golang, Python and NodeJs Database: ✅ SQL Databases: Postgres, MySQL, MSSQL, and SQLite. ✅ NoSQL Databases: MongoDB, Redis, Elasticsearch, and DynamoDB. Cloud Data Pipeline: ✅ Proficient in GCP services: Bigquery and Looker Studio for data storage and analysis. ✅ Experienced in AWS services: Step Function, Lambda Function, Athena, EC2, ECS, Glue, S3, Iceberg, Redshift, and EMR Data Stack: ✅ Big Data Tools: Proficient in Spark, Kafka, and Flink for handling large-scale data processing. ✅ DBT (Data Build Tool): Skilled in DBT for transforming and modeling data. ✅ Airflow: Experienced in using Apache Airflow for orchestrating data workflows. ✅ Containerization: Familiar with Docker and Kubernetes for containerization and orchestration. ✅ Version Control: Proficient in Git, Gitlab, and GitHub for collaborative development and version control. Web Scraping/Automation: ✅ Proficient in web scraping using Scrapy, BeautifulSoup, Selenium, and Playwright. I am dedicated to delivering high-quality data solutions and ensuring data accuracy and reliability. With my extensive skill set and experience, I am confident in my ability to tackle complex data engineering challenges and provide valuable insights to clients. If you're looking for a data engineer who can streamline your data processes, create efficient data pipelines, and turn your data into actionable insights, feel free to contact me. Let's discuss how I can help you achieve your data goals.

I’m a Senior Data Engineer with a background that runs through clinical operations, regulated healthcare environments, and financial data systems. The non-linear path is intentional – and it’s the reason I bring something most engineers can’t. Before data engineering, I was pre-med at Augusta University. Organic chem, biochem, microbiology – the full sequence. Then years working in and around regulated clinical settings before moving into data infrastructure. Healthcare data isn’t a domain I picked up for a role. The science came first. The work: production ELT pipelines in Snowflake and AWS, analytics-ready data models built in dbt, governed datasets designed for regulated environments. Recent projects include SEC XBRL financial data pipelines, healthcare claims transformations, and synthetic EMR modeling with FHIR-compliant output. I care about correctness and downstream reliability. I build systems that hold up under scrutiny – clean staging layers, clear grain definitions, defensible business logic, and documentation that explains why, not just what. If you need a data engineer who understands the clinical context behind the data, not just the tooling, let’s talk.

You can search for me in Linked In by my name as Leonard De Lanerole. I’m a Data Engineer specialising in Snowflake, dbt, Matillion, and Azure, with hands‑on experience delivering scalable analytics platforms across sports, telecom, and enterprise environments. I design and build end‑to‑end data solutions that transform raw data into trusted, analytics‑ready insights. My core expertise includes Snowflake and dbt Cloud modelling, Amazon Redshift data warehousing (raw, curated, and reporting layers), and Matillion ETL for reliable batch pipelines. I’ve worked extensively with Azure Data Factory for orchestration, Azure Logic Apps for integrations and automation, and Power BI for delivering business‑ready reporting. I am Microsoft Azure certified and experienced in production support, performance optimisation, and building maintainable data architectures. I bring a practical, detail‑driven approach and can own data pipelines end‑to‑end—from ingestion to analytics and reporting.

If your data pipeline is broken, slow, or nonexistent, I will build it. I'm a data engineer specialising in ETL/ELT pipeline design, real-time data ingestion, and analytics infrastructure. I work with Python, SQL, Apache Kafka, dbt, PostgreSQL, BigQuery, and PySpark to build data systems that run in production — not just demos. What I can do for you: Design and build ELT pipelines using Airflow, dbt, and BigQuery – staging, intermediate, and mart layers with incremental models and schema tests Real-time data ingestion and streaming with Apache Kafka and PySpark Structured Streaming — micro-batch processing, JSON parsing, and time-series storage RAG chatbots and document Q&A systems using LangChain, ChromaDB, and Streamlit — semantic search, source citations, multi-turn memory SQL analytics and query optimisation across PostgreSQL, BigQuery, and MySQL — window functions, CTEs, recursive queries, stored procedures Grafana dashboards for live operational monitoring connected to TimescaleDB hypertables Python automation for data ingestion, transformation, and pipeline orchestration using Pandas, NumPy, and SQLAlchemy Tableau and Power BI dashboards for KPI tracking and business reporting Data quality management: validation, anomaly detection, schema enforcement built into pipelines from day one Dimensional modeling and data warehouse design for scalable analytics What makes my pipelines different: I don't just write scripts — I build systems with failure handling, retry logic, schema validation, and documentation. Every project I deliver includes a README with architecture decisions, how to run it locally, and why I made the technical choices I did. Clients don't get black boxes. Recent projects (all on GitHub): → Real-Time Sales Pipeline — Kafka + PySpark + TimescaleDB + Grafana. Python producer sends 2 synthetic orders/sec to Kafka. PySpark Structured Streaming reads, parses JSON, and writes to TimescaleDB hypertables. 3-panel Grafana dashboard: live revenue gauge, orders/min time series, and product breakdown pie chart. 138+ rows confirmed end-to-end. Full-stack containerised via Docker Compose. → E-Commerce ELT Pipeline — Airflow + dbt + BigQuery on 99K+ Brazilian Olist e-commerce orders. Airflow 2.8.1 orchestration, dbt transformations across staging → intermediate → mart layers with incremental models and schema tests, BigQuery warehouse, fully containerised. Production-grade pipeline design with documented architecture. → RAG Document Chatbot — LangChain + ChromaDB + Streamlit. Ingests PDFs, chunks semantically using Hugging Face embeddings, stores in ChromaDB vector store, and serves answers via LangChain Q&A chain with source citations and conversation memory. Clean Streamlit UI with adjustable chunk size and retrieval count sliders. Runs locally via Ollama — no external API dependency. Tech stack: Data Engineering: Apache Kafka · PySpark · dbt · Airflow · ETL/ELT · TimescaleDB · HDFS · Spark SQL Databases: PostgreSQL · MySQL · BigQuery · TimescaleDB Languages: Python (Pandas, NumPy, SQLAlchemy) · SQL · PL/pgSQL · Spark SQL Visualisation: Tableau · Power BI · Grafana · Matplotlib Cloud: Azure · Google Cloud (BigQuery, GCS) Tools: Git · Docker · PowerShell · Dimensional Modeling Background: P.G. Diploma in Big Data Solution Architecture (Conestoga College, Canada) and M.E. in Computer Engineering (GTU). International experience — studied and worked in Canada: async-first communication, clear documentation, and on-time delivery. If you need someone who builds data systems that actually run in production, let's talk.

I am a GCP & AWS Professional Data Engineer with approximately 3 years of experience specializing in Data Engineering, ETL development, and Data Warehouse solutions. ✨ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴𝘀 ☁️ Cloud Platforms Expertise: Proficient in Google Cloud Platform (GCP), Azure, and AWS for building scalable and reliable cloud solutions. 🔄 Data Integration & ETL Pipelines: Expertise in automating ETL processes using Airflow, Talend, IBM DataStage, and other tools to seamlessly integrate data from various sources. 🏗️ Data Transformation & Modeling: Leveraging DBT, DataForm, and SQL to transform and model data for robust reporting and analytics. 🏢 Data Warehousing & Data Lakes: Skilled in designing and managing scalable data warehousing solutions and implementing data lakes for organized raw data storage. 📊 Dashboard and Reporting: Experienced in creating dynamic dashboards and reports using Looker, Power BI, Tableau, Excel, Sigma, and Retool. 📈 KPI Calculation and Extraction: Specialized in calculating and extracting key performance indicators to support strategic business decisions. 🔧 Cloud Automation & DevOps: Proficient in Terraform for infrastructure as code, automating cloud resource management, and deploying efficient cloud solutions. 💻 Custom Scripts & Automation: Developing Python scripts and Google AppScripts for automating repetitive tasks and streamlining processes. 📱 Custom Applications & Interfaces: Building tailored applications using Retool, integrating them with data systems to enhance business operations. 🔍 Data Solution Architecture: Designing data architecture solutions that align with business goals, ensuring a cohesive and scalable data ecosystem. ✨ 𝗞𝗲𝘆 𝗔𝗰𝗰𝗼𝗺𝗽𝗹𝗶𝘀𝗵𝗺𝗲𝗻𝘁𝘀 ⏱️ Real-Time Data Pipeline: Reduced data processing time from hours to minutes for a client by implementing a data pipeline using BigQuery, Airflow, and Cloud Composer, enabling timely business decisions. 🚚 Data Migration: Successfully migrated a client’s data infrastructure from on-premises to GCP, utilizing Terraform for automated deployments, reducing operational costs, and improving system reliability. 🎯 Customer Data Integration: Consolidated customer data into a single source of truth for a marketing team using a CDC mechanism, enhancing campaign targeting and tracking. 📊 Marketing Data Automation: Automated data ingestion and built a data warehouse for a marketing agency, creating webhooks to detect lead data changes and integrating multiple sources into a centralized data warehouse. 🔄 Legacy System Transition: Led the transition from a legacy POS system to a modern salon management platform, automating workflows and enhancing business data operations using Python and SQL. 📅 Automated Airflow Metadata Extraction: Developed an automated solution for extracting Airflow metadata and integrating it with Confluence, significantly streamlining the process. 📧 Morning Report Automation: Designed an automated system to verify daily data loads, sending consolidated email reports, which reduced manual effort and ensured timely verification. ✨ 𝗦𝗞𝗜𝗟𝗟𝗦 🖥️ Programming Languages: • Python, • R, • SQL ☁️ Cloud Platforms: • Google Cloud Platform (GCP), • Amazon Web Services (AWS), • Microsoft Azure 🔧 Data Engineering Tools: • BigQuery, • GCS, • Pub/Sub, • Cloud Composer, • Compute Engine, • Redshift, • S3, • Glue, • Step Functions,• Synapses, • Blob Storage, • Databricks, • Data Factory 📜 Infrastructure as Code: • Terraform 🔗 Data Integration Tools: • Stitch, • API Integration, • CDC Mechanisms 🏢 Data Warehousing: • BigQuery, • Amazon Redshift, • Azure Synapse Analytics ⚙️ Workflow Orchestration: • Google Cloud Composer, • AWS Step Functions, • Azure Data Factory 📊 Data Visualization & BI Tools: • Looker Studio, • Power BI, • Tableau, • Excel 🚀 Automation & DevOps: • GitHub for CI/CD, • Kubernetes, • Cloud Automation 💼 CRM & Data Automation: • Retool, • HubSpot, • Zapier, • SuperMetrix 💽 Database Management: • SQL Server, • MySQL, • MongoDB ✨ 𝗪𝗵𝘆 𝗛𝗶𝗿𝗲 𝗺𝗲 ✅ Proven Track Record: Demonstrated success in delivering high-quality, scalable data solutions that drive business growth and efficiency. 🛠️ Technical Expertise: In-depth knowledge of cloud platforms, ETL tools, and data engineering practices, ensuring top-tier technical solutions. 🤝 Client-Centric Approach: Focused on understanding and addressing unique business challenges through tailored data strategies. 💡 Innovative Solutions: Constantly staying ahead of industry trends and implementing cutting-edge technologies like AI and machine learning. 📈 Results-Driven: Committed to delivering measurable outcomes, enhancing decision-making, and operational efficiency. 🌟 Industry Specialization: Specializing in sectors such as E-commerce, Finance, and Marketing, providing targeted expertise and solutions.

Hi, I’m an Analytics Engineer and Data Analyst who helps businesses turn raw, messy data into structured, reliable data systems and actionable insights. I don’t just build dashboards — I design and build the data pipelines and models that power them. 🔧 What I can help you with: • Build end-to-end Power BI dashboards with KPIs and business logic • Design and implement data models (star schema, fact & dimension tables) • Clean, transform, and structure data using SQL and Python (Pandas) • Write advanced SQL queries for analytics and reporting • Build ETL pipelines and data workflows (Python / dbt basics) • Analyze business performance across SaaS, e-commerce, healthcare, and finance 🧠 Core Strengths: • Data Modeling & Analytics Engineering mindset • Strong SQL-based analytical thinking • Data cleaning, transformation, and pipeline design • Business-focused KPI development • Translating raw data into decision-ready insights 📊 Tools & Technologies: Power BI • SQL • Python (Pandas) • dbt • Excel • Git • Basic ETL workflows 📁 Selected Projects: • Customer 360 Analytics Platform (Data Modeling + Multi-source Data Design) • SaaS Funnel & Revenue Analytics Dashboard (Conversion + Retention Analysis) • Healthcare Operations KPI Dashboard (Business Performance Tracking) • Stock Market Analytics Project (Python + dbt + Power BI pipeline) I focus on building structured, scalable, and reliable analytics solutions that help businesses understand their customers, revenue, and operations clearly. 📩 Let’s connect and turn your data into a system that drives decisions — not just visuals.

Most data pipelines don’t fail because of code. They fail because they weren't built for scale. With 5+ years of experience engineering data systems at companies like Danone and Zurich, I help businesses transform fragile prototypes into resilient, production-grade infrastructure. I don’t just move data; I build the "Source of Truth" that leadership and AI systems actually trust. ➔ Productionizing AI Pipelines: Hardening Python prototypes into scalable RAG and LLM infrastructures (Azure). ➔ Infrastructure-as-Code: Building automated, modular ETL/ELT pipelines that don't require daily manual fixes. ➔ The "One-Source" Dashboard: Integrating messy data from APIs, SaaS (Shopify, HubSpot), and databases into clean Snowflake/BigQuery layers. ➔ Performance Recovery: Optimizing slow SQL queries and high-cost cloud warehouses to save you thousands in monthly spend. ➔ Technical Writing for Data & AI Teams: Creating product documentation, implementation guides, architecture documentation, data dictionaries, knowledge bases, and thought leadership content that makes complex systems easier to understand and adopt. 🛠 Tech Stack Languages: Python (FastAPI, Pandas, PySpark), SQL Data Engineering: ETL/ELT Pipelines, Data Warehousing, Data Modeling, Data Quality, Data Governance Cloud & Warehousing: Snowflake, BigQuery, Databricks, Azure Data Factory, Azure Data Lake, AWS (S3, Athena, Glue) Orchestration & Transformation: Apache Airflow, dbt Analytics & BI: Tableau, Power BI Development & Collaboration: Git, GitHub, VS Code Data Ops: API Integrations, Data Validation, Workflow Automation Technical Writing: Product Documentation, API Documentation, User Guides, Knowledge Bases, Data Dictionaries, Technical Blog Content ✅ Why Me? 5+ Years Experience: I've seen what breaks at the enterprise level and how to prevent it in your startup. Hands-On Builder & Technical Writer: I can both build the system and explain it clearly to engineers, stakeholders, and customers. Speed over Perfection: I focus on shipping high-impact systems that drive revenue, not just technical documentation. Transparent Communication: You get regular updates and a partner who challenges requirements to find better solutions. Ready to clean up your data debt?

I am a Data Engineer with over 6 years of experience in architecting, developing, and optimizing data pipelines and warehousing solutions. My expertise spans SQL, Python, Google Cloud Platform, dbt, and Airflow, allowing me to create robust and scalable technical solutions that drive data-driven initiatives. I excel at collaborating with cross-functional teams to translate complex business requirements into actionable insights. Whether you're looking to enhance existing infrastructure or build new data systems from the ground up, I can deliver tailored solutions that meet your unique needs. Let's connect and explore how I can contribute to your project's success and drive significant business impact through advanced data engineering.

Analytics Engineer with 4+ years of experience designing end-to-end data platforms, modern data warehouses, and automated BI systems across enterprise environments combining with a solid academic foundation in Data Science (3 published research papers in ML/Big Data). Proven track record in transforming complex and operational data into analytics-ready data using cutting-edge technologies and cloud-native solutions (GCP, Azure, AWS). Strong expertise in data modeling, pipeline orchestration, performance optimization, deliver automation that enabled real-time, data-driven decision-making for big corps like Coca-Cola, VinGroup, FPT. Passionate about building reliable, scalable, business-aligned data solutions.

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