Title: Analytics Engineer | dbt • SQL • BigQuery | Turning Raw Data into Insights Overview: Having refined my data skills within hyper-scale e-commerce and analytics environments like Amazon and leading enterprise platforms, I bridge the gap between data engineering and business intelligence. I help businesses transform messy SQL queries, broken dashboards, and untrusted data into clean, production-ready, and well-modeled data layers. As an Analytics Engineer, I apply software engineering best practices to data transformation. I don't just write scripts; I build version-controlled, tested, and documented data models using dbt and SQL, ensuring your business analysts and stakeholders always work with a single source of truth. 🛠️ What I Do Best: Data Transformation & Modeling: Designing clean, scalable Star/Snowflake schemas and dimensional data marts. Modern Data Stack (MDS) Implementation: Leveraging dbt (data build tool) to modularize, test, and automate your SQL pipelines. Data Quality Assurance: Implementing rigorous testing, data profiling, and documentation to eliminate "bad data" before it reaches your reports. Cloud Data Warehousing: Optimizing enterprise data warehouses (Google BigQuery) for peak query performance and cost efficiency. 💻 Technical Toolkit: Core: Advanced SQL (CTEs, Window Functions, Query Optimization), Python. Frameworks & Tools: dbt (Core/Cloud), Git/GitHub for version control. Cloud Infrastructure: Google Cloud Platform (BigQuery, GCS), Docker containerization. Big Data Ecosystem: Hands-on experience with Apache Spark for large-scale data ingestion and processing. 🌟 Why Hire Me? I treat data like software. Every model I build is optimized for cost, designed for speed, and documented so clearly that your internal team can manage it effortlessly moving forward. Let's clean up your data debt and unlock the true value of your data infrastructure. Click the "Invite to Job" button, and let’s discuss your project today!

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

I'm an AI/ML Engineer and Data Scientist with 15+ years of experience building intelligent AI solutions and scalable data pipelines. I specialize in production-grade GenAI systems, advanced analytics, and enterprise-scale data infrastructure. I build end-to-end solutions - from big data pipelines processing terabytes to RAG systems and predictive ML models - that transform raw data into actionable intelligence and automated business value. 💡 Core Competencies: ✨ Generative AI & LLMs - Expert in GPT, Claude, Gemini Pro, LLaMA, Mistral. Specialized in prompt engineering, fine-tuning, and building production GenAI applications. ✨ RAG Systems & Vector Databases - Architecting advanced Retrieval-Augmented Generation pipelines using Pinecone, Weaviate, ChromaDB, Qdrant, Milvus. Expert in semantic search, chunking strategies, and hybrid retrieval systems. ✨ Machine Learning & Data Science - Developing predictive models, recommendation systems, time series forecasting, anomaly detection, and classification solutions. Deep expertise in feature engineering, model optimization, and statistical analysis. ✨ Big Data Pipelines & Engineering - Building robust ETL/ELT pipelines processing terabytes using Apache Spark, Airflow, Kafka, and modern data stack. Expert in data warehousing (Snowflake, Redshift, BigQuery), streaming, and distributed computing. ✨ MLOps & Production Systems - Deploying enterprise ML pipelines with monitoring, CI/CD automation, and scalable infrastructure using Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP). 🛠 Technical Stack: ML & Data Science: TensorFlow, PyTorch, Scikit-learn, XGBoost, CNNs, Transformers, Computer Vision, NLP, Time Series Generative AI: GPT-4o, Claude, LLaMA, LangChain, LangGraph, LlamaIndex, RAG, Vector Search, Fine-tuning Voice AI: Whisper, ElevenLabs, PlayHT, Deepgram, AssemblyAI, Speech-to-Text, Text-to-Speech, Voice Cloning, Real-time Voice Agents Big Data: Apache Spark, Airflow, Kafka, Flink, Databricks, dbt, Delta Lake, Real-time Streaming Data Infrastructure: Snowflake, Redshift, BigQuery, PostgreSQL, MongoDB, Redis, S3, Data Lakes Vector DBs: Pinecone, Weaviate, ChromaDB, Qdrant, Milvus, FAISS MLOps: MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, Docker, Kubernetes, Model Monitoring Analytics: Pandas, NumPy, Tableau, Power BI, Jupyter, Statistical Modeling Languages: Python, SQL, Scala, PySpark 🎖 Key Achievements: • Architected big data platform processing 2TB+ daily across distributed pipelines, reducing processing time by 75% • Implemented RAG system improving information retrieval accuracy by 85% and reducing response time by 70% • Deployed GenAI solution processing 500K+ daily requests with intelligent caching, reducing LLM costs by 55% • Created predictive models increasing revenue forecasting accuracy by 40% and enabling proactive business decisions • Designed data lakehouse architecture unifying structured and unstructured data, enabling advanced analytics for 500+ users • Developed automated feature engineering pipeline reducing model development time by 60% and improving feature quality • Implemented ML monitoring system detecting model drift and data quality issues, maintaining model performance in production 🚀 What Sets Me Apart: → Full-Stack Data & AI Expertise: From data ingestion to ML model deployment, I handle the complete data science and engineering workflow → Production-First Mindset: Building scalable, maintainable systems designed for reliability, performance, and real-world business conditions → Business-Focused Solutions: Every technical implementation is aligned with measurable business outcomes and ROI → Data Quality Obsession: Ensuring data integrity, governance, and observability across all pipelines and models → Clear Communication: Translating complex technical and statistical concepts into actionable insights for technical and non-technical stakeholders → Continuous Learning: Staying current with the latest advances in AI, ML research, and data engineering best practices Ready to transform your data into intelligent, automated systems. Let's discuss your goals!

I’m a Data Engineer with a strong background in modern data stack development, analytics engineering, big data processing, and AI-powered automation. I specialize in building scalable ETL/ELT pipelines, data warehouses, analytics platforms, and cloud-based data solutions that transform raw data into reliable business insights. My experience spans data engineering, business intelligence, automation, and modern analytics workflows. Core Expertise: • Snowflake, BigQuery, PostgreSQL • dbt, Airflow, Fivetran • Apache Spark, Kafka • Python, SQL, API Integrations • Data Modeling & Warehouse Architecture • ETL / ELT Pipeline Development • Azure Data Lake Storage Gen2 • Azure Synapse Analytics • Databricks • AI Workflows & Automation • Tableau, Power BI, Looker, DOMO At this point, I’ve worked with a wide range of modern data technologies and continuously expand my expertise as the data ecosystem evolves. Highlights: ✔ Processed 1M+ records through scalable ETL/ELT pipelines ✔ Reduced manual reporting work by 20+ hours per week through automation ✔ Designed scalable analytics layers supporting 1,000+ dashboard users ✔ Built data lakes, warehouses, and analytics-ready datasets ✔ Developed event-driven architectures using Kafka and Spark ✔ Experience across data engineering, analytics, business intelligence, and automation projects I focus on building reliable, maintainable systems that turn messy data into clean, trusted infrastructure teams can actually use. Whether you need a data pipeline, data warehouse, analytics solution, cloud data architecture, or AI-powered automation, I’m ready to help. Let’s build things your team actually wants to use.

I've spent 15+ years as a Senior Data Platform Engineer building and maintaining production reporting systems for multi-tenant SaaS and industrial environments. I help data-intensive SaaS companies modernize and stabilize large database systems by optimizing slow SQL, execution plans, indexing strategies, ETL workflows, and warehouse performance across PostgreSQL, MySQL, Snowflake, and SQL Server. My strongest work is in performance-heavy environments where reporting, analytics, pricing logic, monitoring, and automated decision-making depend on clean, fast, reliable data. I have optimized long-running production queries, redesigned indexing and archival strategies, improved ETL/data synchronization flows, and supported modernization of large operational databases without disrupting live products. 𝗪𝗵𝗮𝘁 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝘀𝗮𝘆: "Optimized our data models and indexing strategy, cutting report generation time while rolling out to new clients with zero issues." "Built and owned the automated publishing pipeline that now reliably serves our full client reporting fleet." "Clear ownership and proactive monitoring during our platform modernization - exactly the engineer we needed." 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ✔ Design and optimize complex queries, views, stored procedures, and data models for large-scale production reporting ✔ Execution plan analysis, indexing strategy, and performance tuning across multi-tenant environments ✔ Lead SQL Server version migrations and schema modernization with full compatibility and regression testing ✔ Maintain and extend optimized view/indexing architectures mid-rollout while preserving stability 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 & 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 ✔ Operate and extend GitHub-based workbook duplication/publishing pipelines using Python, REST APIs, and GitHub Actions ✔ Manage extract scheduling against production OLTP databases and minimize refresh load ✔ Maintain and deploy Tableau workbooks across dev/UAT/production supporting ~100 per-client reports ✔ Tableau Server administration, extract monitoring, and proactive root-cause resolution for failures or degradation ✔ Support smooth transition of reporting workloads to in-app platforms while keeping Tableau reliable 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗖𝗿𝗼𝘀𝘀-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 ✔ Implement proactive monitoring (New Relic / equivalent) to detect and resolve issues before client impact ✔ Strong troubleshooting with clear communication to technical and business stakeholders on performance and risk ✔ Collaborate with Data Engineering, Infrastructure, and domain experts during platform migrations and architectural decisions 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗛𝗶𝗿𝗲 𝗠𝗲 ✔ Full end-to-end ownership of production reporting stacks - SQL optimization, automation pipelines, and operational reliability ✔ Proven experience with multi-tenant client reporting environments and regulated industrial data (directly applicable to healthcare compliance) ✔ Deep expertise in database migrations, Python/GitHub automation, and performance tuning - exactly the skills for your SQL Server modernization ✔ Licensed P.Eng with 15+ years delivering stable, high-trust production systems with clear ownership and honest timelines I work best with teams modernizing SQL Server environments or scaling Tableau operations who need a hands-on engineer to complete remediation work, stabilize the platform, and keep reporting fast and trusted. Send me a message with your current migration status and pipeline pain points - I’ll outline exactly how I’d approach finishing the work with zero client impact. 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: PostgreSQL, MySQL, Snowflake, Yellowbrick-compatible warehouse tuning, execution plan analysis, query rewrites, indexing, memory tuning, ETL optimization, large data footprints, SaaS modernization, pricing analytics, eCommerce intelligence, brand monitoring data.

I help businesses turn messy data into automated dashboards, reporting systems, and decision-ready insights using Power BI, SQL, Excel, Python, Tableau, dbt, and Google Sheets. Over the last 3 years, I’ve worked across data analysis, business intelligence, dashboard development, reporting automation, and analytics engineering to help companies track KPIs, automate reporting, uncover revenue leaks, improve visibility, and build scalable systems teams actually use. If you are spending hours updating spreadsheets manually, struggling with unreliable reports, or sitting on data that never turns into actionable insights, I can help. WHAT I BUILD: Dashboard Development & Business Intelligence Interactive Power BI, Tableau, and Looker Studio dashboards for sales, finance, operations, marketing, customer analytics, KPI tracking, and executive reporting. Data Cleaning & Transformation Cleaning, structuring, validating, and transforming datasets across Excel, SQL databases, APIs, CSV files, and Google Sheets using Power Query, SQL, Python Pandas, and ETL workflows. Reporting Automation & Excel Systems Excel automation, VBA macros, Power Query workflows, KPI trackers, Google Sheets automation, and reporting pipelines that reduce manual work and improve reporting accuracy. SQL & Data Analysis SQL reporting, data extraction, ETL pipelines, database analysis, forecasting, churn analysis, cohort analysis, funnel tracking, customer analytics, and operational reporting. Statistical Analysis & Predictive Analytics EDA, regression analysis, A/B testing, forecasting models, machine learning workflows, predictive analytics, and statistical analysis using Python, R, scikit-learn, Pandas, and NumPy. REAL WORK: Built automated dashboards that reduced reporting time from hours to minutes. Developed KPI and executive dashboards for startups, e-commerce brands, and small businesses. Cleaned and transformed large datasets for forecasting, analytics, and operational reporting. Built Excel and Google Sheets automation systems that eliminated repetitive manual reporting processes. TOOLS & TECHNOLOGIES: Power BI, SQL, Excel, Python, Tableau, Looker Studio, Google Sheets, dbt, Power Query, VBA, Pandas, NumPy, scikit-learn, ETL Pipelines, Reporting Automation, Data Visualization, Business Intelligence. If you need dashboards, reporting automation, forecasting, business intelligence, KPI reporting, SQL analysis, Excel automation, or data analytics that supports real business decisions, send me a message and let’s discuss your project. Data Analyst, Data Analysis, Data Analytics, Business Intelligence, BI Developer, Analytics Engineer, Reporting Analyst, Data Visualization, Dashboard Developer, Dashboard Design, KPI Dashboard, Executive Dashboard, Automated Dashboard, Business Dashboard, Power BI Dashboard, Tableau Dashboard, Interactive Dashboard, Reporting Automation, Business Reporting, Data Insights, Business Insights, Performance Tracking, KPI Reporting, Data Cleaning, Data Transformation, Data Wrangling, Data Extraction, ETL, ETL Pipeline, Data Pipeline, Database Analysis, SQL, MySQL, PostgreSQL, SQL Server, BigQuery, dbt, Power BI, DAX, Power Query, Microsoft Excel, Excel Automation, VBA, Google Sheets, Tableau, Looker Studio, Python, Pandas, NumPy, Data Automation, Workflow Automation, Statistical Analysis, Predictive Analytics, Forecasting, Financial Analysis, Sales Analysis, Marketing Analytics, Customer Analytics, Revenue Analytics, Cohort Analysis, Funnel Analysis, A/B Testing, Machine Learning, Data Science, Operational Analytics, Executive Reporting, Business Intelligence Dashboard, Analytics Dashboard, Automated Reports, Custom Dashboard, Data Consultant, Business Performance Analysis, Reporting Solutions, Actionable Insights, Data Modeling, Data Mining, Data Processing, Data Management, Reporting Systems, Data Storytelling, Dashboard Expert, Real Time Dashboard, Financial Dashboard, Marketing Dashboard, SQL Reporting, Excel Dashboard, Spreadsheet Automation, Data Integration, Data Quality, Data Warehouse, Reporting Solutions, Business Analytics Expert, SaaS Analytics, Ecommerce Analytics, Power BI Automation, Dashboard Automation, Advanced Analytics, Data Driven Decisions, Analytics Strategy, Executive Dashboard, KPI Tracking, Automated Reporting, Data Automation, Workflow Automation, Data Modeling, Revenue Analytics, Customer Analytics, Sales Analytics, Financial Reporting, Predictive Modeling, Churn Analysis, Cohort Analysis, Funnel Analysis, Interactive Data Visualization, Operational Analytics, Reporting Pipelines, Data Engineering, Analytics Engineering, Business Intelligence Consultant, Analytics Dashboard Development, Data Reporting Automation Expert, Data Visualization Specialist, Power BI Reports Development, SQL Data Reporting, Business KPI Reporting, Data Strategy, Data Architecture Design, Cloud Data Analytics, Data Systems Development, Decision Intelligence, Data-Driven Reporting Systems, Business

Hi, I'm a Data & Analytics Engineer with 3+ years of experience helping businesses transform raw data into reliable datasets, dashboards, and actionable insights. I specialize in designing and building end-to-end data solutions, from data ingestion and transformation to analytics-ready reporting layers. My experience includes working with large datasets, optimizing SQL queries, developing scalable ETL/ELT pipelines, and implementing modern data architectures. My expertise includes: ✔ Databricks & PySpark ✔ SQL & Query Optimization ✔ dbt (Data Build Tool) ✔ Azure Data Factory (ADF) ✔ Azure Data Lake Storage (ADLS) ✔ Data Modeling & Analytics Engineering ✔ ETL/ELT Pipeline Development ✔ Data Validation & Quality Checks ✔ Power BI & Business Reporting Over the past 3+ years, I have worked on analytics and data engineering projects involving financial, travel, and business reporting data. I have built data pipelines, implemented transformation frameworks, optimized data processing workloads, and delivered trusted datasets for reporting and decision-making. What you can expect when working with me: • Clear and proactive communication • Reliable and maintainable solutions • Focus on data quality and accuracy • Timely delivery • Business-oriented approach to problem-solving Whether you need a data pipeline, data warehouse solution, Databricks implementation, dbt project, reporting dataset, or SQL optimization, I would be happy to discuss your requirements and help you achieve your goals. Let's connect and build something valuable together.

I'm the person you bring in when your team has data but no one trusts the dashboards, or has dashboards but no one uses them. Two and a half years on BigQuery + dbt as the core stack, with Looker Studio and QuickSight on top, and Redshift experience on the AWS side. AI shows up in two distinct places in my work: inside the team to ship faster and with fewer errors, and on the consumption side via MCP so non-technical users can self-serve. Currently leading the data work at a company selling across sales ops, inventory, attendance, and ticketing/B2B. What I'm useful for: * A dbt + BigQuery project from scratch, or untangling one that's grown organically and lost trust * Dashboards that answer the questions your team actually asks in standups, not the ones a template assumes * Exposing analytics through APIs when an internal tool or product needs trusted numbers without a BI dashboard in front * Self-service / agentic analytics via MCP, so non-technical teammates and clients can ask data questions in plain language instead of filing tickets and waiting on a one-off report * Automations and AI integrations inside the data team's own workflow (CI tests, generated docs, AI-assisted modeling and review), so the team ships faster and with fewer errors * Cost work on existing BigQuery setups (most of what I see is running 2-3x what it needs to) Domains I know well: * Sales and revenue: funnels, conversion, cohort retention, simple forecasting * Inventory and ops: turnover, stockouts, replenishment * Workforce: shifts, presence, productivity * Tickets and B2B: visitor cohorts, LTV, retention curves How I work: short loops, written updates, plain language with non-technical stakeholders. I ask a lot of questions about the business before touching the warehouse, which usually saves a week or two of building the wrong thing. To start a conversation, send: 1. What you sell and to whom 2. One or two decisions you keep making blind because the data isn't there yet I'll come back with a concrete plan for what to build first and what to skip.

I am a cloud-native data engineer with over a decade of hands-on experience building high-performance data platforms, pipelines, and warehouses at scale. I've led end-to-end data architecture projects for top Fortune 100 companies l ✅ Modern ETL/ELT pipelines using various tools like Apache Airflow, Apache Beam, DBT, Dataflow, AWS Glue, Kinesis, SQLMesh and many more ✅ Cloud platforms (Google Cloud & AWS): BigQuery, EMR, Redshift, S3, Kinesis, Dataflow, Pub/Sub ✅ Privacy-first architectures with pseudonymization and de-identification (DLP) ✅ Data lakes & warehouses built from scratch, supporting ML, analytics & compliance ✅ CI/CD automation with Terraform, GitHub Actions, Jenkins, CloudBuild ✅ Stream + batch processing, dimensional modeling, Looker/Google Data Studio dashboards I don’t just build pipelines — I build resilient data ecosystems that power ML models, dashboards, business decisions, and customer intelligence. Whether you’re a startup building from scratch or an enterprise optimizing your stack, I bring deep experience, cross-functional collaboration, and cloud-certified expertise (GCP, AWS, Oracle, ITIL) to the table. 📈 Let’s turn your raw data into reliable insights. Ready when you are.

I am a former finance consultant and operations manager turned data engineer. I combine my technical expertise in data management with my business knowledge to help you access and leverage your data. I can help you in the various stages of your data life-cycle : - Ingestion : finding the right tools to automate data extraction, whether from a local database, a cloud provider or a file system (Python, Airbyte, Fivetran). - Transformation : building and orchestrating the ETL/ELT pipelines to clean and transform your data (DBT, dataform, Airflow). - Service to business users : helping you find the right BI tool to access your data for analytical purposes (Looker studio, streamlit, Metabase) - Analytics : helping you analyze your data to uncover trends and make more informed decisions (exploratory analyses using python, SQL or Excel and machine learning using scikit-learn)