I help businesses clean, structure, analyze, and visualize their data using Excel, Power BI, SQL, and modern analytics tools. My background combines engineering problem-solving with hands-on data analytics training. I have 4+ years of professional experience in technical systems, troubleshooting, process optimization, and working with structured information in industrial environments. I am currently specializing in data analytics through a hands-on program covering SQL, BigQuery, DBT, ELT workflows, Looker Studio, Power BI, DAX, Python, Pandas, and machine learning fundamentals. I can help with: - Excel data cleaning and formatting - Data mapping and category standardization - Spreadsheet structuring - Power BI dashboards and KPI reports - Looker Studio reports - SQL queries and business analysis - CSV / Excel transformation - Dashboard-ready dataset preparation - Data validation and quality checks My strength is turning messy or unstructured data into a clean, usable format for reporting, analysis, or business decision-making. I focus on clear communication, organized deliverables, and practical solutions that are easy for clients to maintain after delivery.

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 turn complex, messy data into scalable products and clear business decisions. With 10+ years across pharma and commercial analytics, I've built data models and pipelines that powered supply chain optimization, pricing strategy, financial reconciliation, and clinical trial operations. At AstraZeneca I lead greenfield clinical data product design across 4 countries. At Eseotres Pharma I delivered 115–120% sales target achievement, recovered $23M MXN through automated reconciliation, and reduced year-end overstock by 95%, all grounded in solid data modeling and engineering. I work across the full stack: Fivetran, Snowflake, dbt, Databricks, Power BI, Python, SQL, and I translate between technical teams and business stakeholders fluently. Native Spanish, C1 English and French.

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.

------------------------------------------------------------------------------------------------------------------ 🌐Professional Profile ------------------------------------------------------------------------------------------------------------------ Data Engineer/Data Analyst/Data Scientist with extensive experience in developing analytical solutions and data pipelines, optimizing visualization and managing large datasets to enhance decision-making in corporate environments. Proficient in Power BI, Python, SQL, and machine learning, with strong skills in data source integration and predictive modeling to extract valuable and strategic insights. ------------------------------------------------------------------------------------------------------------------ 🌐Core Skills ------------------------------------------------------------------------------------------------------------------ 🗝️Data Analysis and Visualization: ✳️Designed and developed advanced Power BI dashboards using DAX (Measures, Calculated Columns, Calculated Tables, advanced functions like CALCULATE, FILTER, LOOKUPVALUE, and Time Intelligence). ✳️Created impactful visualizations to transform raw data into actionable insights, facilitating interpretation and strategic decision-making. ✳️Implemented security in Power BI with Row-Level Security (RLS) for segmented access to interactive reports. 🗝️Data Integration and Processing: ✳️Managed efficient connections to various data sources, including Databricks, BigQuery, Snowflake, SQL Server, MySQL, JSON, Google Sheets, and Excel files through Power Query. ✳️Performed data transformations and manipulations using Python (pandas and numpy), optimizing workflows and ensuring data quality. ✳️Created optimized SQL views for platforms like BigQuery and Databricks, enhancing data accessibility and usability for analysis. 🗝️Data Engineering and Machine Learning: ✳️Developed data pipelines in Python for ETL processes in environments like PostgreSQL and Snowflake. ✳️Applied machine learning techniques (scikit-learn) in advanced analysis and predictive models, increasing the strategic value of data. ✳️Integrated predictive models into decision-making processes, providing accurate and timely analytical tools for the organization. ------------------------------------------------------------------------------------------------------------------ 🌐Professional Experience ------------------------------------------------------------------------------------------------------------------ 📈📈EQT Corporation — Data Engineer/Data Analyst Senior 📆Remote, USA | September 2023 – September 2024 ✳️Developed SQL views in Databricks and created data pipelines in Python, structuring and optimizing queries with CTEs. ✳️Designed Power BI reports, published to Power BI Service, and utilized deployment pipelines for continuous, up-to-date reporting. ✳️Enhanced data accessibility and performance to support informed decision-making in Salesforce through Azure Function integration. 📈📈Synovia Digital — Data Engineer/Data Analyst Middle 📆Remote, Ireland | October 2022 – August 2023 ✳️Automated real-time data extraction from SAP HANA using Azure Functions and Azure Data Factory. ✳️Transformed data into JSON format, stored in Azure Blob Storage, achieving efficient data management. ✳️Integrated data into Snowflake, created standardized schemas, and customized Power BI visualizations with access security for over 120 users. 📈📈Nvend — Data Engineer/Data Scientist/Data Analyst 📆Remote, USA | November 2021 – August 2022 ✳️Developed Python pipelines for competitive benchmarking, improving strategic analysis. ✳️Built a machine learning model on a dataset of 33 million records to predict Sales Volume, optimizing accuracy and supporting data-driven sales forecasting. 📈📈BrandAlignment — Data Analyst 📆Remote, USA | July 2019 – August 2021 ✳️Designed Power BI sales dashboards to monitor inventory and sales trends, for internal and competitive analysis. ✳️Connected to various data sources (Snowflake, Azure SQL Server, AWS, Excel) ensuring accurate and integrated cross-platform data analysis. ------------------------------------------------------------------------------------------------------------------

I have spent 8 years at the intersection of data, AI, and the question nobody wants to ask “does it actually deliver results?” From forecasting systems to LLM pipelines and autonomous agents built for real world problems where off-the-shelf solutions fail. The tools change with every project. The bar doesn't. Here is an overview of my Stack 𝗠𝗟 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, CatBoost, statsmodels 𝗟𝗟𝗠𝘀 & 𝗡𝗟𝗣: Open AI, Claude, Gemini, Grok, LLaMA, Mistral, DeepSeek, BERT, BART, SetFit, HuggingFace 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: LangChain, LangGraph, RAG Pipelines, n8n, Make, OpenAI API, Anthropic API, Lovable, OpenClaw 𝗩𝗲𝗰𝘁𝗼𝗿 & 𝗦𝗲𝗮𝗿𝗰𝗵: Pinecone, FAISS, ChromaDB, SentenceTransformers, Embeddings 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: pandas, NumPy, Parquet, Airflow, dbt, ETL Pipelines 𝗔𝗣𝗜𝘀 & 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴: FastAPI, Flask, WebSocket, PRAW, BeautifulSoup, Selenium 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Matplotlib, Seaborn, Plotly, Tableau, PowerBI, SHAP 𝗖𝗹𝗼𝘂𝗱 & 𝗜𝗻𝗳𝗿𝗮: AWS EC2, SageMaker, AWS Bedrock, Firebase, Docker, VPS 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 & 𝗔𝗽𝗽𝘀: React, Next.js, Streamlit, Gradio, Lovable 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Gmail API, Google Calendar API, WhatsApp API, Stripe, PayPal, Odoo You can get a feel for the work pretty quickly. Here's a slice. → 𝗔𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • Built a 𝒇𝒖𝒍𝒍-𝒄𝒚𝒄𝒍𝒆 𝑨𝑰 𝒉𝒊𝒓𝒊𝒏𝒈 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 using n8n to orchestrate OpenAI-powered resume parsing with Gmail, Google Sheets, and Calendar APIs reducing 𝐻𝑅 𝑚𝑎𝑛𝑢𝑎𝑙 𝑤𝑜𝑟𝑘𝑙𝑜𝑎𝑑 𝑏𝑦 80% with centralized candidate tracking and automated scheduling. • Developed 𝒂 𝒓𝒆𝒂𝒍-𝒕𝒊𝒎𝒆 𝑨𝑰 𝒗𝒐𝒊𝒄𝒆 𝒂𝒈𝒆𝒏𝒕 supporting voice-to-voice, speech-to-text and text-to-text conversations via FastAPI and WebSocket with ultra low latency using GPT for dialogue management. • Built an 𝑨𝑰 𝒑𝒐𝒘𝒆𝒓𝒆𝒅 𝒕𝒆𝒍𝒆𝒎𝒆𝒅𝒊𝒄𝒊𝒏𝒆 𝒑𝒍𝒂𝒕𝒇𝒐𝒓𝒎 on Next.js and Firebase with role-based AI prompts, automated symptom collection and 𝑟𝑒𝑎𝑙 𝑡𝑖𝑚𝑒 𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑖𝑛𝑠𝑖𝑔ℎ𝑡𝑠 for patient doctor interaction. → 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 From pharmaceutical supply chains to crypto markets, I build forecasting systems that drive real inventory, budget and trading decisions. • Built a 3𝑴+ 𝒓𝒆𝒄𝒐𝒓𝒅 𝒑𝒉𝒂𝒓𝒎𝒂 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒔𝒚𝒔𝒕𝒆𝒎 pipeline: XGBoost R²=0.90, 20% accuracy gain, 17-chart EDA uncovering SKU concentration risk and billing-cycle demand patterns • 𝑪𝒓𝒄𝒓𝒚𝒑𝒕𝒐 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒎𝒐𝒅𝒆𝒍𝒔 using ARIMA + Reddit sentiment (PRAW + SetFit) → BUY/SELL/HOLD signals for BTC, ETH, SOL, DOGE • 𝑫𝒆𝒎𝒂𝒏𝒅 𝒇𝒐𝒓𝒆𝒄𝒂𝒔𝒕𝒊𝒏𝒈 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 (LR, XGBoost, RF, LSTM) achieving R²~0.99 used car price prediction deployed via Flask → 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 I build classification, regression, and validation systems with rigorous evaluation not just accuracy scores but defensible, 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏-𝒓𝒆𝒂𝒅𝒚 𝒎𝒐𝒅𝒆𝒍𝒔. • SVM, Gradient Boosting, MLP, XGBoost, Logistic Regression always with GridSearch and KFold CV for hyperparameter integrity • Diabetes detection: 86% accuracy on 3-class imbalanced clinical dataset with feature engineering and undersampling experiments → 𝗡𝗟𝗣 & 𝗟𝗟𝗠-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 I combine classical text modeling with modern LLMs to extract structured insight from unstructured data at scale. • Claude 3.5 Sonnet (AWS Bedrock) + BART MNLI + SentenceTransformer pipeline quantifying open ended survey sentiment for fragrance product strategy • Real-time Reddit 𝒔𝒆𝒏𝒕𝒊𝒎𝒆𝒏𝒕 𝒅𝒂𝒔𝒉𝒃𝒐𝒂𝒓𝒅 for ASTS ticker upvote-weighted transformer scoring with daily trend visualization • 𝑻𝒆𝒙𝒕 𝑪𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒓 across disaster tweets (TFIDF, 80%), IMDB reviews (LSTM, 86%) and news categorization (CNN + GloVe, 75%) • GPT-4o, Claude, LLaMA, Grok and Mistral used as deliberate data enrichment and annotation tools inside ML pipelines I work with startups building their first AI product, enterprises with complex data problems, and individuals with unique challenges nobody else wants to touch. If the problem is hard and the data is messy that's exactly where I do my best work. Send me a message and let's figure out if I'm the right fit. I will tell you within 24 hours whether I can help and how.

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

🔍 Expert Data Scientist and Analytics Engineer With extensive experience in data science, analytics, and engineering, I am your go-to expert for turning complex data into actionable insights. As the current technical lead for a boutique consulting company, I have successfully led projects for a diverse range of clients, from large organizations like KPMG and Hagerty Consulting to public sector entities such as New York City Emergency Management. Key Skills and Expertise: Programming: Python, SQL, R Data Visualization: Tableau, PowerBI, Looker, Matplotlib, ggplot Data Engineering: Building robust data pipelines, ETL processes (dbt, BigQuery, Apache Spark, Hadoop) Machine Learning: Predictive modeling, NLP, sentiment analysis, algorithm development Analytics: Statistical analysis, A/B testing, KPI development, causal inference Technical Tools: Git, HTML/CSS, Power Automate, Tableau Site Administration, Agile (Jira), Process Flow Mapping What I Offer: Comprehensive Data Solutions: From data collection and cleaning to analysis and visualization, I provide end-to-end data solutions tailored to your needs. Actionable Insights: My work focuses on delivering clear, actionable insights that drive decision-making and improve business outcomes. Custom Dashboards and Reports: Using Tableau, PowerBI, or Looker I create intuitive and interactive dashboards that help you monitor key metrics and trends. Technical Leadership: With a proven track record of leading technical teams and projects, I ensure high-quality results delivered on time and within budget. Notable Projects: Cloud Data Warehouse Development: Developed a cloud-based database to manage over 2 million entries, increasing daily processing by 10x for all users. Technologies used: Python, REST APIs, Microsoft Azure SQL Database, Power Automate, Salesforce. Sentiment Analysis for UX Improvement: Implemented an NLP model for sentiment analysis and topic modeling, analyzing over 10,000 posts to extract actionable insights, identifying 80+ feedback points for website improvement. Technologies used: Python, NLTK, Vertex AI. Facebook Ads Optimization: Led Facebook Ads A/B testing initiatives, resulting in a 30% improvement in click-through rates and a 10% increase in conversions for clients. Technologies used: Looker, Google Analytics. I am passionate about leveraging data to solve real-world problems and am eager to bring my expertise to your next project. Let's work together to unlock the full potential of your data! Contact me today to discuss how I can help your business thrive.

I'm a data engineer specialising in building clean, reliable data infrastructure for fast-moving startups and media businesses — without the overhead of a full data team. I've worked across the full data stack: designing Postgres schemas, building ETL pipelines, wiring up GA4 and BigQuery, and delivering Looker Studio dashboards that non-technical stakeholders can actually use. Most recently, I've been the sole data engineer at a SaaS startup in the publishing industry, owning everything from ingestion to reporting — so I understand what it means to work lean and deliver fast. What I can help you with: - ETL pipeline design & automation (Python, Prefect, Airbyte) - Data warehouse setup & optimisation (BigQuery, PostgreSQL) - Analytics engineering (dbt, SQL views, reporting schemas) - BI dashboards (Looker Studio, GA4 integration) - Data audits — map what you have, fix what's broken, build what's missing - API connectors & data ingestion layer design I work well with engineering teams (comfortable in monorepos, TypeScript-heavy stacks, and modern tooling like Drizzle ORM) and I communicate clearly with non-technical founders and stakeholders. If you're a startup that's drowning in raw data but not getting value from it — that's exactly the problem I solve. Let's talk.

With over 20 years of experience as a Senior Data Engineer, I specialize in building robust, scalable data pipelines and end-to-end data solutions that drive real business impact. My expertise spans the full data engineering stack — from ETL pipeline development and real-time streaming to cloud data warehousing and BI dashboards. I have delivered high-performance data solutions across diverse industries including healthcare, financial services, and marketing technology. Whether it's architecting AWS Glue pipelines with PySpark, orchestrating complex workflows in Apache Airflow, implementing real-time CDC with Kafka, or optimizing query performance in Snowflake — I bring deep technical expertise and a results-driven mindset to every engagement. My core strengths include: 🔹 ETL & Pipeline Engineering — Python, AWS Glue, PySpark, Airflow, Informatica 🔹 Real-Time Streaming — Apache Kafka, AWS MSK, CDC, Mirth Connect 🔹 Cloud & Data Warehousing — AWS (S3, Lambda, Glue), Snowflake, PostgreSQL, MySQL 🔹 BI & Analytics — Looker, Streamlit, dbt 🔹 Healthcare Data — HL7, RDF, SPARQL, Epic Systems Beyond technical skills, I bring strong leadership and collaboration experience, having worked with cross-functional teams across Cognizant, Infosys, and leading US healthcare and fintech organizations. I hold an MIT Applied Data Science certification and am an Oracle Certified Java Professional, ensuring my skills stay current with industry standards. I am passionate about turning complex data challenges into clean, efficient, and scalable solutions. If you're looking for a seasoned data engineer who delivers quality, reliability, and innovation — let's connect!