James Hewitson
Head Enterprise Data Architect @NatWest Group
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WORK HISTORY
Head Enterprise Data Architect @NatWest Group
GB
EDUCATION
Northumbria University
Business with Finance, Finance
ABOUT JAMES HEWITSON
Head Architect and Principal data engineer that collaborates with multi-national companies in solving real world business problems across different data domains. 12+ years’ experience in client facing operational, consultancy and sales positions. Strong programming coding ability, both in producing clean and efficient code. Talent to deliver and develop real-time and batch processing data pipelines. Strength evolving software, statistical, cloud and data engineering solutions with industry cutting edge technologies. Exemplify excellent Data Modelling, batch/stream processing and Database/Data Warehousing practices. Proficient in Machine Learning and Deep Learning for multiple applications including Natural Language Processing. Strive to find key hidden insights which help to gain quantifiable impact for companies.Data Skillset:• Architecture: Data Modelling, System/ Data Analysis, Design and Development• Big Data: Kafka, Spark & Talend• Cloud Computing: Amazon Web Services (AWS x 4 certifications), Google Cloud Platform (GCP x 1 certification) & Microsoft Azure• Data Engineering: Data Ingestion, Data Management(Design, create, test), Data Warehouses(Redshift, Big Query, Snowflake), ETL pipelines, SQL(PostgreSQL), NoSQL(Cassandra), Deploy productionised data solutions, Virtualization(Denondo), Airflow• Data Science: Curious about Data, problem solver & influence without authority• Data Virtualization: Denodo, Pneuron• Data Visualization: Matplotlib, Seaborn, Bokeh, Plotly• Deep Learning: Artificial Neural Networks(ANNs), Convolutional Neural Network(CNN), Recurrent Neural Network (RNN), Autoencoders, Keras & Tensorflow• ITIL: Risk Management(Identify, Assess & Control risks), Incident Management(Prioritise, Investigate & Resolution of incidents) Change Management (Design, Develop & Deploy changes)• Machine Learning: Automatic Machine Learning, Regression, Classification, Clustering, Model Optimization, Supervised & Unsupervised Learning• Natural Language Processing (NLP): Sentiment Analysis, Topic Modelling, Gensim, Spacy & NLTK packages • Programming: Python, Java & Scala• Software Development: Agile, Scrum, Containerisation (Docker/Kubernetes), End-to-end deployment process, CI/CD Deployment, Jira, Git/Gitlab, Bitbucket & Unit Testing/Troubleshooting • Statistics & Applied Mathematics: Hypothesis Testing, Probability, Sampling, Estimation, Correlation, Descriptive Statistics, Cumulative Distribution, Continuous Distribution
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