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Hire VaidotasVaidotas Kanopa
Verified Expert in Engineering
Data Engineer, Data Scientist, and Developer
Vilnius, Vilnius County, Lithuania
Toptal member since May 26, 2020
Bio
Vaidotas is a senior data engineer and data scientist with experience in architecting highly scalable data platforms and end-to-end BI solutions. He specializes in modern data stacks, including AWS, Snowflake, Airflow, and dbt. Backed by a master's degree in financial mathematics and a first-place finish in the National Mathematical Olympiad, Vaidotas combines elite analytical capabilities with strong business acumen to solve complex enterprise data challenges.
Portfolio
Verto Commerce, MB
Amazon DynamoDB, Amazon S3 (AWS S3), Amazon Athena, Microsoft Power BI, Python...
kevin.
Snowflake, Python, Apache Airflow, Data Build Tool (dbt), Amazon S3 (AWS S3)...
Logdirect
Data Modeling, Data Analytics, Talend ETL, Jupyter Notebook, Microsoft Excel...
Experience
- Data Engineering - 12 years
- Amazon Web Services (AWS) - 8 years
- Data Science - 8 years
- ETL - 8 years
- Python - 8 years
- SQL - 7 years
- Microsoft Power BI - 4 years
- SQL Server Analysis Services (SSAS) - 4 years
Work Experience
Lead Data Engineer & Architect
2023 - PRESENT
Verto Commerce, MB
- Engineered a scalable, serverless AWS architecture using Lambda, DynamoDB, and RDS to power back-end services for subscription-based apps serving thousands of active users.
- Built an automated event-tracking pipeline ingesting millions of raw visitor data points into Amazon S3 to enable granular journey mapping and A/B testing analytics.
- Developed Python-based ETL pipelines to process raw S3 data streams into structured, query-ready Amazon Athena tables for downstream business intelligence.
- Architected enterprise Power BI data models and executive dashboards, converting raw marketing and operational data into actionable KPI metrics for management.
- Implemented automated data quality monitoring and error-tracking pipelines across third-party APIs (Stripe, Klaviyo), reducing system discrepancies and preventing customer chargebacks.
- Architected a full-stack, multi-agent AI platform (FastAPI, Svelte, OpenAI) orchestrating three distinct LLMs for conversational routing, dynamic summarization, and task evaluation.
- Engineered LLM-driven state-transition logic to autonomously validate user inputs against strict database criteria and manage workflow progression.
Technologies: Amazon DynamoDB, Amazon S3 (AWS S3), Amazon Athena, Microsoft Power BI, Python, Vue 3, Quasar, Svelte, Cloudflare, AWS Lambda, Amazon RDS, Stripe API, Facebook API, Facebook Ads, Google Ads, Klaviyo, JavaScript, PostgreSQL, APIs, Analytics, Reporting, HubSpot API, Generative Artificial Intelligence (GenAI), DAX, Dashboards, Artificial Intelligence (AI), TypeScript, Large Language Models (LLMs), API Integration, ChatGPT, Database Management, Data Integration, Reports, Google Sheets, Marketing, Google Analytics, Web Development, Data Extraction, Data Quality, PySpark, eCommerce, Consumer Products, Metrics, Amazon Web Services (AWS), Amazon Kinesis, Architecture, Database Architecture, Retrieval-augmented Generation (RAG), Acquisition Analysis, A/B Testing, Growth Marketing, Growth Strategy, LTV:CAC, Lifetime Value (LTV), Marketing Analytics
Data Engineer
2022 - 2023
kevin.
- Designed and implemented foundational data models for a POS system integrated with an open banking solution, which had previously secured $65 million in funding. These models supported critical analytics and scalability for the product’s success.
- Built and optimized tables in Snowflake to support structured data storage, query efficiency, and accessibility for analytics and reporting.
- Developed automated ETL pipelines using Apache Airflow and DBT, managing event-based data ingestion, transformations, and analytical table creation.
- Integrated data quality checks within Airflow using DBT, creating proactive alerts for data discrepancies or unexpected values and enabling issue resolution prior to daily report distribution.
- Partnered with the data analytics team to refine and optimize data tables in Snowflake, ensuring data structure supported Tableau reporting used by top management.
- Helped optimize the overall data ingestion structure from S3 into Snowflake using Apache Airflow.
Technologies: Snowflake, Python, Apache Airflow, Data Build Tool (dbt), Amazon S3 (AWS S3), Analytics, Data Architecture, Reporting, Dashboards, Data Integration, dbt Cloud, Reports, Google Sheets, Data Extraction, Data Quality, Metrics, Amazon Web Services (AWS), Database Architecture
Data Engineer | Data Scientist
2018 - 2022
Logdirect
- Co-created an AI-powered customer support system that automatically handled 1,000 to 5,000 requests per day. The production system was implemented using Salesforce Einstein.
- Built an MVP customer message classification long short-term memory networks (LSTM) model with Python Keras and scikit-learn. This proof of concept led to a decision to launch the customer support automation project into production.
- Created a pipeline to extract and transform over one million email messages into a clean and ML-ready dataset using the Python Pandas library and text manipulation techniques.
- Developed and maintained Microsoft SQL Server Analysis Services (MS SSAS) multidimensional cubes that the majority of the company used as the source for business insights and day-to-day analytics.
- Participated in developing and maintaining an Amazon Redshift data warehouse that was used as the primary data source for the whole company.
- Refactored ETL processes to provide data for day-to-day use and monitoring. This reduced data loading errors by 70% and helped spot and correct countless data inconsistencies.
- Created Power BI reports providing business insights and monitoring, such as payment reports used by the payments team to spot problems in payment flows, GDPR tracking reports, ETL process monitoring, and an automated customer support report.
Technologies: Data Modeling, Data Analytics, Talend ETL, Jupyter Notebook, Microsoft Excel, Business Intelligence (BI), Keras, TensorFlow, Deep Learning, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL, NumPy, Data Science, Pandas, Python, Machine Learning, Microsoft Power BI, Redshift, Microsoft SQL Server, Talend, Microsoft Data Transformation Services (now SSIS), Salesforce Einstein, Data Engineering, ETL, Data Warehousing, Relational Databases, Amazon Web Services (AWS), Databases, Data Cleansing, API Integration, APIs, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Data, Database Design, Dimensional Modeling, Git, Data Visualization, Scikit-learn, JSON, Data Pipelines, Snowflake, BigQuery, Analytics, Data Architecture, Reporting, Google Cloud, Data Scientist, DAX, Dashboards, Data Integration, Reports, Google Sheets, Marketing, Amazon Redshift, Data Extraction, Data Quality, eCommerce, Consumer Products, Metrics, Grafana, Time Series, Architecture, Data Migration, Database Architecture, SQL Server
Data Engineer & ML Specialist
2017 - 2018
Self-employed
- Architected an end-to-end visual inspection system to detect product anomalies (e.g., wine residues, shattered glass), achieving human-level accuracy and processing 20 evaluations per second.
- Engineered real-time OpenCV data pipelines to extract and preprocess live feeds directly from industrial camera hardware.
- Integrated Keras deep learning models with computer vision algorithms, targeting specific image regions to maximize detection precision and model efficiency.
- Developed a Kivy-based operational interface, enabling non-technical assembly line staff to monitor pipeline outputs and run inspections.
- Partnered with assembly line managers to translate physical defect specifications into machine learning training parameters and pipeline requirements.
Technologies: PyCharm, Convolutional Neural Networks (CNNs), Image Processing, Jupyter, Jupyter Notebook, Computer Vision, Keras, OpenCV, NumPy, Data Science, Pandas, Python, Machine Learning, Kivy, Git, Reporting, Artificial Intelligence (AI), Reports, Google Sheets
Director of Commerce
2016 - 2018
UAB Daivida
- Managed the company's largest client portfolio (accounting for 40% of total revenue) and negotiated key enterprise accounts—including a major EU retail chain—driving a 25% YoY revenue increase.
- Pioneered a data-driven strategy by building custom QlikView BI dashboards to track the complete sales lifecycle and deliver real-time operational metrics to executive leadership.
- Automated ERP data extraction by developing VBA scripts to query the core MS SQL database, reducing manual reporting time by 50% and eliminating data-entry errors.
Technologies: Excel VBA, Microsoft Excel, Business Intelligence (BI), SQL, Microsoft SQL Server, QlikView, ETL, Relational Databases, Databases, Data Cleansing, Data Visualization, Google Sheets, Excel Macros, Data Extraction, Metrics
Founder & eCommerce Data Engineer
2015 - 2018
MB Morsas
- Executed data-driven conversion rate optimization (CRO) strategies for Amazon listings, analyzing search algorithms to scale a core product to a #1 best-seller ranking in a major category.
- Architected scalable eCommerce infrastructure on WooCommerce and Shopify, managing databases and integrations to support thousands of active customers.
- Developed a semi-automated workflow system for customer service operations, increasing agent efficiency and ticket resolution capacity by 60%.
- Analyzed supply chain logistics and negotiated with overseas vendors to ensure strict quality control and optimize delivery timelines.
- Engineered data pipelines in Python to aggregate and analyze Facebook Ads performance and eCommerce metrics, significantly accelerating product A/B testing and media buying decisions.
Technologies: HTML, Landing Page Optimization, Ad Optimization, WooCommerce, PHP, Python, Facebook Ads, Google Ads, Shopify, MySQL, Google Sheets, Marketing, Google Analytics, AWS Lambda, Excel Macros, eCommerce, Metrics, Growth Marketing, Marketing Analytics
Analytics Engineer
2017 - 2017
Self-employed
- Engineered Python (Pandas) ETL pipelines to extract and transform legacy MS SQL ERP data and flat files into a centralized, BI-ready data model.
- Developed interactive QlikView and Tableau dashboards utilized by executive management and the entire sales team to track core KPIs and drive strategic decision-making.
- Analyzed complex manufacturing datasets to optimize production workflows, detecting recipe inconsistencies and evaluating the financial ROI of sales promotions.
- Automated reporting workflows, replacing manual spreadsheet exports with dynamic visual reporting that directly answered ad-hoc business inquiries.
Technologies: Excel VBA, Data Analysis, Microsoft Excel, Business Intelligence (BI), SQL, Data Science, Python, Microsoft SQL Server, QlikView, Data Cleansing, Tableau, Data Engineering, Data, Dimensional Modeling, Data Visualization, Analytics, Matplotlib, Reports, Metrics
Purchasing Manager
2015 - 2016
UAB Daivida
- Leveraged data insights to optimize the supply chain, negotiating a 10–15% cost reduction on major raw materials while ensuring continuous manufacturing operations.
- Programmed a semi-automated inventory forecasting system using Excel VBA, streamlining stock evaluation and significantly reducing manual purchasing workflows.
- Engineered SQL-based ETL scripts to extract purchasing and inventory data from the enterprise ERP, developing QlikView BI dashboards to monitor demand, pricing, and process anomalies.
Technologies: Excel VBA, Microsoft Excel, Business Intelligence (BI), Microsoft SQL Server, QlikView, Excel Macros, Data Extraction
Senior Data Specialist
2013 - 2015
VĮ "Žemės Ūkio Informacijos ir Kaimo Verslo Centras
- Developed statistical models in R (including Linear Regressions and BLUP) to optimize genetic evaluation and selective animal breeding programs.
- Engineered Python ETL pipelines to systematically extract and process large-scale agricultural datasets from enterprise Oracle databases.
- Authored high-impact governmental analytics reports, implementing strict data quality, validation, and accuracy standards for national agricultural data.
Technologies: Statistical Modeling, Statistics, R, Microsoft Excel, Business Intelligence (BI), Data Science, Python, Machine Learning, Oracle SQL, ETL, Relational Databases, Databases, Data Cleansing, Oracle, Data Engineering, Data, Metrics
Experience
AI-driven Email Classifier & Autoresponder
Context and Impact: Automated the classification and response generation for 1,000–5,000 daily customer inquiries, capturing previously lost sales opportunities and significantly reducing support overhead.
Technical Execution:
• Data Engineering: Built pipelines to extract, aggregate, and transform historical Salesforce requests into ML-ready training datasets.
• Model Development: Prototyped the initial NLP classification model using Python, TensorFlow, and Keras before training and fine-tuning the production model in Salesforce Einstein.
• System Integration: Co-developed an adjustable Salesforce interface to manage automated responses and ensure seamless system integration.
• Analytics: Developed reporting dashboards to track model accuracy, system performance, and overall business value.
Technical Execution:
• Data Engineering: Built pipelines to extract, aggregate, and transform historical Salesforce requests into ML-ready training datasets.
• Model Development: Prototyped the initial NLP classification model using Python, TensorFlow, and Keras before training and fine-tuning the production model in Salesforce Einstein.
• System Integration: Co-developed an adjustable Salesforce interface to manage automated responses and ensure seamless system integration.
• Analytics: Developed reporting dashboards to track model accuracy, system performance, and overall business value.
AWS BI Infrastructure & Data Warehouse Migration
Context and Impact: Migrated legacy on-premises BI infrastructure to AWS, resolving scalability bottlenecks, reducing infrastructure costs, and accelerating data processing with virtually zero downtime.
Technical Execution:
• Cloud Architecture: Co-designed and provisioned the target AWS environment, utilizing EC2, RDS, and Amazon Redshift for scalable analytics workloads.
• Database and SSAS Migration: Architected target database schemas and executed the seamless migration of BI data and Microsoft SSAS tabular/multidimensional models to the cloud.
• ETL Engineering: Refactored and developed modernized ETL pipelines using Talend to orchestrate data flows within the new AWS ecosystem.
• Deployment Strategy: Implemented a phased migration and cutover strategy, ensuring continuous, uninterrupted business intelligence operations during the transition.
Technical Execution:
• Cloud Architecture: Co-designed and provisioned the target AWS environment, utilizing EC2, RDS, and Amazon Redshift for scalable analytics workloads.
• Database and SSAS Migration: Architected target database schemas and executed the seamless migration of BI data and Microsoft SSAS tabular/multidimensional models to the cloud.
• ETL Engineering: Refactored and developed modernized ETL pipelines using Talend to orchestrate data flows within the new AWS ecosystem.
• Deployment Strategy: Implemented a phased migration and cutover strategy, ensuring continuous, uninterrupted business intelligence operations during the transition.
Enterprise Data Warehouse & Analytics Democratization
Context and Impact: Eliminated departmental data silos by establishing a centralized, single-source-of-truth Data Warehouse on AWS Redshift, standardizing enterprise reporting, and ensuring data consistency across the organization.
Technical Execution:
• Data Warehouse Architecture: Engineered centralized data storage utilizing AWS Redshift and Redshift Spectrum to aggregate and query dispersed operational data sources.
• Analytics Standardization: Architected a structured Power BI ecosystem, migrating, optimizing, and consolidating disparate legacy reporting tools into a unified governance framework.
• Business Intelligence: Developed and deployed automated Power BI dashboards tailored to cross-departmental business needs, accelerating company-wide access to reliable data.
Technical Execution:
• Data Warehouse Architecture: Engineered centralized data storage utilizing AWS Redshift and Redshift Spectrum to aggregate and query dispersed operational data sources.
• Analytics Standardization: Architected a structured Power BI ecosystem, migrating, optimizing, and consolidating disparate legacy reporting tools into a unified governance framework.
• Business Intelligence: Developed and deployed automated Power BI dashboards tailored to cross-departmental business needs, accelerating company-wide access to reliable data.
Real-time Computer Vision Quality Control System
Context and Impact: Replaced costly, error-prone manual inspection in wine manufacturing with an automated computer vision pipeline, achieving human-level defect detection accuracy at a real-time processing speed of 20 evaluations per second.
Technical Execution:
• Computer Vision Engine: Engineered a Python and OpenCV-based image processing pipeline to dynamically transform, analyze, and evaluate live visual feeds against reference images.
• Hardware Ingestion Pipeline: Integrated the software directly with industrial-grade manufacturing cameras to ensure low-latency, continuous data ingestion from the assembly line.
• Scalable Architecture: Designed a highly adaptable, product-agnostic logic framework, allowing rapid recalibration for new production lines and bottle variations.
• Operational Interface: Developed a lightweight, Kivy-based GUI to enable non-technical plant operators to monitor real-time pipeline outputs and adjust inspection parameters.
Technical Execution:
• Computer Vision Engine: Engineered a Python and OpenCV-based image processing pipeline to dynamically transform, analyze, and evaluate live visual feeds against reference images.
• Hardware Ingestion Pipeline: Integrated the software directly with industrial-grade manufacturing cameras to ensure low-latency, continuous data ingestion from the assembly line.
• Scalable Architecture: Designed a highly adaptable, product-agnostic logic framework, allowing rapid recalibration for new production lines and bottle variations.
• Operational Interface: Developed a lightweight, Kivy-based GUI to enable non-technical plant operators to monitor real-time pipeline outputs and adjust inspection parameters.
Serverless AWS Cloud Architecture & Marketplace Platform
Context and Impact: Architected and deployed a highly scalable, fully serverless marketplace platform for B2B acquisitions, eliminating infrastructure management overhead and ensuring seamless, automated scaling during traffic spikes.
Technical Execution:
• Serverless Cloud Architecture: Designed and implemented a robust event-driven back end utilizing AWS Lambda, Amazon API Gateway, and Amazon S3 for dynamic routing, processing, and storage.
• NoSQL Data Layer: Engineered the database infrastructure using Amazon DynamoDB to provide low-latency, highly scalable transactional data management.
• Security and Access Pipelines: Integrated AWS Cognito to construct secure, scalable user authentication, authorization, and identity management workflows.
• Front-end Application Delivery: Developed a reactive Vue.js Single-Page Application (SPA) and optimized global performance and caching utilizing Cloudflare's CDN.
Technical Execution:
• Serverless Cloud Architecture: Designed and implemented a robust event-driven back end utilizing AWS Lambda, Amazon API Gateway, and Amazon S3 for dynamic routing, processing, and storage.
• NoSQL Data Layer: Engineered the database infrastructure using Amazon DynamoDB to provide low-latency, highly scalable transactional data management.
• Security and Access Pipelines: Integrated AWS Cognito to construct secure, scalable user authentication, authorization, and identity management workflows.
• Front-end Application Delivery: Developed a reactive Vue.js Single-Page Application (SPA) and optimized global performance and caching utilizing Cloudflare's CDN.
eCommerce Architecture & Logistics Analytics Platform
Context and Impact: Architected and deployed a fully functional D2C (direct-to-consumer) eCommerce platform, capturing sudden market demand during COVID-19 lockdowns and establishing a net-new digital revenue channel.
Technical Execution:
• eCommerce Infrastructure: Rapidly deployed and configured a scalable WooCommerce infrastructure to process a sudden influx of localized food delivery orders.
• Dynamic Pricing Logic: Engineered custom pricing algorithms and parameter-based rules to accurately calculate costs based on complex, internal meat product attributes (e.g., variable weight pricing).
• Logistics and Constraints Modeling: Programmed flexible delivery-time constraints and logic to manage fulfillment capacity and optimize route scheduling.
• Automated Reporting: Developed customized sales and operational reporting pipelines to provide business leadership with real-time visibility into order volumes, revenue, and fulfillment metrics.
Technical Execution:
• eCommerce Infrastructure: Rapidly deployed and configured a scalable WooCommerce infrastructure to process a sudden influx of localized food delivery orders.
• Dynamic Pricing Logic: Engineered custom pricing algorithms and parameter-based rules to accurately calculate costs based on complex, internal meat product attributes (e.g., variable weight pricing).
• Logistics and Constraints Modeling: Programmed flexible delivery-time constraints and logic to manage fulfillment capacity and optimize route scheduling.
• Automated Reporting: Developed customized sales and operational reporting pipelines to provide business leadership with real-time visibility into order volumes, revenue, and fulfillment metrics.
Automated Market Intelligence & Web Scraping Pipeline
Context and Impact: Automated competitive market analysis to identify high-demand, low-competition product niches, directly driving the investment and launch of top-selling products across all EU Amazon marketplaces.
Technical Execution:
• Data Ingestion and Web Scraping: Engineered an automated Python-based data extraction pipeline utilizing Selenium to continuously scrape live product metrics and competitor data from Amazon.
• API Integration and Data Aggregation: Orchestrated the ingestion and merging of disparate datasets from Google Ads and third-party APIs to map global search trends against current market saturation.
• Algorithmic Evaluation: Developed automated business logic to cross-reference search volume with competitor density, rapidly isolating highly profitable market opportunities at scale.
Technical Execution:
• Data Ingestion and Web Scraping: Engineered an automated Python-based data extraction pipeline utilizing Selenium to continuously scrape live product metrics and competitor data from Amazon.
• API Integration and Data Aggregation: Orchestrated the ingestion and merging of disparate datasets from Google Ads and third-party APIs to map global search trends against current market saturation.
• Algorithmic Evaluation: Developed automated business logic to cross-reference search volume with competitor density, rapidly isolating highly profitable market opportunities at scale.
Multi-agent AI Digital Wellness Platform
Context and Impact: Developed a customer-facing, AI-powered coaching application that guided users through structured behavioral health courses, utilizing a multi-agent architecture to automate conversational routing, progress evaluation, and continuous context management.
Technical Execution:
• Multi-Agent Orchestration: Designed a FastAPI back-end integrating OpenAI and OpenRouter APIs to power a distinct three-agent LLM system (Conversational Agent, Evaluator, Summarizer).
• State and Logic Evaluation: Engineered an Evaluator agent to dynamically validate user inputs against strict progression criteria stored in PostgreSQL, controlling state transitions through course modules.
• Context Optimization: Implemented a continuous Summarizer agent to compress state and chat history after every interaction, ensuring optimal token usage and precise context window management.
• Full-stack Integration: Built a reactive front end using Svelte, securing the application with Auth0, and automating subscription processing via Stripe.
Technical Execution:
• Multi-Agent Orchestration: Designed a FastAPI back-end integrating OpenAI and OpenRouter APIs to power a distinct three-agent LLM system (Conversational Agent, Evaluator, Summarizer).
• State and Logic Evaluation: Engineered an Evaluator agent to dynamically validate user inputs against strict progression criteria stored in PostgreSQL, controlling state transitions through course modules.
• Context Optimization: Implemented a continuous Summarizer agent to compress state and chat history after every interaction, ensuring optimal token usage and precise context window management.
• Full-stack Integration: Built a reactive front end using Svelte, securing the application with Auth0, and automating subscription processing via Stripe.
Education
2009 - 2013
Master's Degree in Financial Mathematics and Statistics
University of Warwick - United Kingdom
Certifications
SEPTEMBER 2022 - SEPTEMBER 2025
AWS Certified Data Analytics Specialty
AWS
JULY 2022 - JULY 2025
AWS Certified Cloud Practitioner
Amazon Web Services Training and Certification
DECEMBER 2020 - PRESENT
Machine Learning Scientist with Python – Career Track
DataCamp Inc.
JUNE 2020 - PRESENT
Python Programmer Track – Career Track
DataCamp Inc.
MAY 2020 - PRESENT
Data Analyst with SQL Server – Career Track
DataCamp Inc.
NOVEMBER 2018 - PRESENT
Deep Learning
deeplearning.ai | via Coursera
OCTOBER 2018 - PRESENT
Big Data
University of California San Diego | via Coursera
SEPTEMBER 2018 - PRESENT
Machine Learning
University of Washington | via Coursera
Skills
Libraries/APIs
Pandas, Scikit-learn, OpenCV, NumPy, Keras, TensorFlow, HubSpot API, PySpark, Kivy, Vue, Vue 3, Stripe API, Facebook API, Matplotlib
Tools
Microsoft Power BI, Microsoft Excel, dbt Cloud, Tableau, Talend ETL, Git, BigQuery, ChatGPT, Google Sheets, Google Analytics, PyCharm, Jupyter, Salesforce Einstein, Amazon Cognito, AWS Glue, Amazon Athena, Amazon Redshift Spectrum, AWS DataSync, Apache Airflow, Grafana, Auth0
Languages
Python, SQL, Snowflake, HTML, Excel VBA, TypeScript, R, C++, PHP, JavaScript
Paradigms
Business Intelligence (BI), ETL, Dimensional Modeling, Database Design
Platforms
Jupyter Notebook, Amazon Web Services (AWS), QlikView, WooCommerce, AWS Lambda, Google Ads, Windows, Talend, Shopify, Oracle, Salesforce, Klaviyo, Docker
Storage
SQL Server Analysis Services (SSAS), SQL Server Integration Services (SSIS), Redshift, Relational Databases, Databases, Data Pipelines, Data Integration, Database Architecture, SQL Server Reporting Services (SSRS), PostgreSQL, Amazon S3 (AWS S3), JSON, Google Cloud, Database Management, Microsoft SQL Server, Oracle SQL, Amazon DynamoDB, MySQL
Industry Expertise
Marketing
Frameworks
Selenium, Quasar, Svelte
Other
Machine Learning, Data Engineering, Data Science, Data Analysis, Data Analytics, Data Modeling, Data Warehousing, Data Cleansing, API Integration, Data, Data Visualization, Analytics, Reporting, Dashboards, Artificial Intelligence (AI), Reports, Data Extraction, Data Quality, eCommerce, Consumer Products, Metrics, Architecture, Full-stack Development, Acquisition Analysis, A/B Testing, Growth Marketing, LTV:CAC, Lifetime Value (LTV), Marketing Analytics, Deep Learning, Convolutional Neural Networks (CNNs), Facebook Ads, Ad Optimization, Landing Page Optimization, Natural Language Processing (NLP), Computer Vision, Image Processing, Statistics, APIs, Amazon RDS, Amazon Kinesis, Generative Pre-trained Transformers (GPT), Data Build Tool (dbt), Data Architecture, Data Scientist, DAX, Amazon Redshift, Excel Macros, Web Development, Time Series, Data Migration, SQL Server, AI Agents, FastAPI, Retrieval-augmented Generation (RAG), Growth Strategy, Microsoft Data Transformation Services (now SSIS), Statistical Modeling, Cloud, Cloudflare, Amazon API Gateway, Data Warehouse Design, Web Scraping, AWS Database Migration Service (DMS), Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), OpenAI
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