Data Tableau

Linda

Email ID: adhrbg@r.postjobfree.com

Contact# 571-***-****

Summary

Experienced as a Tableau Developer/SQL Developer in providing production-level support in business analytics and business intelligence with domain knowledge and experience in Fashion, Healthcare, Finance, and Securities industries.

Proficient in data warehousing, report development, Statistical Modeling, Data Mining, Machine Learning, and Data Visualization.

Experienced in querying data from Microsoft SQL Server (2019), utilizing complex Structured Query Language (SQL), manipulating large unstructured and structured datasets to construct insightful solutions to complex problems and presenting the results in visually engaging and intuitive reports and dashboards.

Proficient in T-SQL, including query optimization/tuning, usage of views, triggers, user-defined functions (UDF), stored procedures, dynamic SQL, common table expression (CTE), temp table, table variable and a variety of joins.

A profound experience of data Extraction, Transforming and Loading (ETL), development of ETL packages, incremental loading, and data cleaning in SSIS.

In-depth understanding of building and publishing interactive report solutions with customized parameters, user filters, and easy-to-read dashboards in Tableau and Power BI.

Proficient in creating different types of reports like a parameterized report, Ad-hoc report, dashboard report, drill down report using Tableau.

Adept experience in creating Tableau dashboards, using heat maps, treemaps, circle views, bar charts, lines, pie charts, area charts, bubbles and highlight tables, symbol and filled maps according to deliverable specifications.

Experienced in Python programming for descriptive, inferential, predictive and descriptive data analyses, using Python libraries such as Numpy, Pandas, SciPy and Scikit-Learn, as well as data visualization packages such as Matplotlib and Seaborn.

Experienced in the entire data project life cycle including Data Acquisition, Data Cleansing, Data Manipulation, Visualization, Feature Engineering, Modelling, Testing, Optimization, etc.

Proficient at Machine Learning algorithms and Predictive Modeling such as Linear Regression, Logistic Regression, Naïve Bayes, Decision Tree, SVM, KNN, K-means clustering, Principal Component Analysis (PCA) etc. and strong knowledge of statistics Methodologies such as Hypothesis Testing and AB Testing.

Familiar with the Hadoop ecosystem and Apache Spark framework such as HDFS, Map-reduce, Hive, SparkSQL, and PySpark.

Knowledge and experience in Cloud Services Amazon Web Services (AWS) and Microsoft Azure such as EC2, RDS, S3, and Azure HDinsight, Machine Learning Studio to assist with big data tools, solve storage issues and work on deployment solution.

Outstanding problem solving and analytical skills to accomplish critical business objectives in a team-oriented environment independently or in a team.

Technical Skills

Programming Languages: SQL, T-SQL, Python(2.x/3.x)

Data warehouse: MySQL, MS SQL Server 2012/2014/2016/2017/2019, AWS Redshift, Oracle Server, Snowflakes

Tools: SQL Server Management Studio, SQL Server Integration Services (SSIS), SQL Server Data Tools (SSDT)

Data Visualizations and Reporting: Tableau Desktop Version (8.x/9.x/10.x), Power BI, SSRS, MS Excel

Big Data: Hadoop 2, HDFS, HIVE, Data Bricks, Spark 2.p

Cloud Services: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform

Machine Learning Algorithms: Linear Regression, Naïve Bayes, Logistic Regression, K-nearest Neighbors (KNN), K-means Clustering, Decision Tree, Support Vector Machine (SVM), Neural Network, PCA

Professional Experience

Mansur Gavriel LLC New York, NY

Tableau Developer/SQL Developer Aug 2020 - Current

Mansur Gavriel is an affordable luxury fashion brand based in New York, US, specializing in women's bags, ready-to-wear fashion, footwear, and accessories. The project is to manage the transaction database, build bag cost data mart for business analysis and create visual reports in Tableau. By visualizing the cost and inventory of bags, the management team can adjust prices better to increase eCommerce sales.

Responsibilities:

Validated and collected data from multiple sources (excel, CSV, vendor-provided API, Oracle Server, SQL Server), and loaded data from these sources to the invoice database by incremental loading in SSIS.

Used efficient queries like temp tables, Common Table Expression (CTE), Stored Procedures, and user-defined functions to query data and involved in SQL query performance tuning and optimization by changing table lock types, adding indexes and applying temp table, etc.

Designed bag production cost data mart by applying dimensional modeling and star schema. Parameterized all possible SQL identity keys in SSIS to make the packages dynamic for configuration.

Designed and created SSIS packages in Visual Studio to extract, transform and load (ETL) production and shipping data from the invoice database and migrate into the cost data mart by developing queries using case statements, stored procedures, CTEs, user-defined functions, and views to translate business requirements and embedded the code into SSIS ETL pipelines.

Scheduled data refresh jobs in SQL Server Agent using T-SQL for daily and weekly incremental loading, daily database backup and restoring, and error handling mechanisms.

Created objects like Views, Stored Procedure, DML Triggers, Transactions and User-Defined Functions with T-SQL in SQL Server 2019 to query data for reporting.

Transformed all reports and dashboards previously built in Excel into Tableau and built dashboards with different charts like bar chart, line chart, geographical map, and applied parameters, quick/context/global filters, and calculated fields in Tableau to deliver cost & inventory status reports.

Working with team of developers to design, develop and implement a BI solution for Sales, Product and Customer KPIs.

Developed business dashboard with multiple panels and parameters and Tableau workbooks to perform year over year, quarter over quarter, YTD, QTD and MTD type of analysis.

Automatically refreshed data in Tableau Server for weekly and monthly increments based on the new charges to ensure that the views and dashboards display updated data.

Environment: MS SQL Server 2019, MS BI Suite (SSIS), Tableau 2020.x (Desktop/Server), Visual Studio, Jira

Healthcare Financial Systems Inc. Hollywood, FL

Tableau Developer/SQL Developer May 2018 - Jun 2020

Healthcare Financial Systems Inc. delivers a web-based Electronic Health Record System that enables ambulatory care physicians and clinical staff to schedule and manage patient appointments, document patient visits, and manage payment and billings. The project is to collect data from different medical facilities in FL, load the data into a data warehouse for further analysis, create visual reports and predict canceling probability to help medical personnel perform their tasks more efficiently and effectively.

Responsibilities:

Used T-SQL in SQL Server Management System (SSMS) to develop complex stored procedures, CTEs, triggers, user-defined functions (UDFs), and views to facilitate ETL processes.

Designed and built pipelines to extract, transform and load (ETL) existing data from the legacy database and new data into data warehouse.

Designed an appointment data warehouse using star schema, defined fact table and dimension tables to meet business requirements and transported stored data from database to data warehouse.

Implemented error handling mechanisms and SQL queries in the packages to ensure ETL performance and data consistency and integrity.

Optimized complex SQL queries and tuned PL/SQL procedures running over 5 million data points by using partition methods and caching strategies, thus reduced page load time by 80% and database reads by 50%.

Generated advanced Tableau dashboards with bar charts, line charts, and text tables, and applied quick/context/global filters, parameters, and calculated fields to help clinical staffs monitor their patients’ appointments.

Supported the development and modification of Tableau views in Tableau, while preparing documentation and training users from respective units.

Scheduled data refresh on Tableau Server for weekly and monthly increments based on business change to ensure that dashboards displayed the most updated information.

Developed machine learning methods including supervised learning such as Logistic Regression and Random forest to predict whether the appointment canceled or not, and used F1 score, precision, recall, and AUC-ROC curve to quantify model performance.

Designed and implemented K-fold and stratified K-fold cross-validation form model testing and selecting based on model significance.

Environment: MS SQL Server 2017, Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn), Tableau (2018.x), AWS

Quant Global Capital Advisors LLC New York, NY

Data Scientist/Tableau Developer Feb 2017 - Apr 2018

Quant Global Capital Advisors is a company in the finance industry that provides investment advisory services. The company offers investment strategy, security selection, and portfolio monitoring services for individual and institutional investors. The project is to collect company’s financial data and rate the company in different investment goals and provide customers an UI dashboard by entering preferences and get recommended stocks.

Responsibilities:

Participated in all phases of data acquisition, data cleaning and processing, model designing and developing, validation and optimization, and delivering interactive reports to clients.

Built a data pipeline with Python to conduct ETL from IEX Cloud API, saving a subscription fee ($6200/year) from buying data online.

Produced large-scale data of financial reports using Pandas, NumPy, Matplotlib and Seaborn libraries to perform Exploratory Data Analysis (EDA) and Data Visualization.

Handled data imbalance using sampling techniques of both down-sampling and up-sampling in Python.

Conducted feature engineering with PCA to reduce dimensions and eliminate overfitting.

Used Seaborn and scikit-learn libraries in Python and developed machine learning methods including supervised learning such as Logistic Regression, Decision Tree and Random forest.

Designed and implemented K-fold and stratified K-fold cross-validation form model testing and selecting based on model significance.

Formulated 2 financial grading structures on the investment preferences and implemented quantitative strategies to filter stocks based on predicted scores.

Connected data with Tableau to perform Data Visualization and deliver customized interactive dashboards based on different investment preferences.

Performed published and maintained reports to help pick up stocks with a high probability of investment. Generated UI Dashboards by entering amount and preferences, then recommend stocks in the order of scores from the model which added an average of 4.7% increase in stock revenue.

Environment: MS SQL Server 2017, Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn), Tableau (2017.x)

Global AI New York, NY

Data Engineer/SQL Developer July 2015 - Jan 2017

Global AI is a Big Data Company which uses statistical and artificial intelligence models to produce actionable insights and signals for institutional clients, including Investors, Governments and Corporations. The project is to develop an application to analyze the loan data for a client company and help improve company’s mortgage business.

Responsibilities:

Worked on cleaning the loan data to ensure data quality, consistency, integrity using Python Pandas and Numpy packages and designed ETL pipeline with PySpark to extract loan data.

Conducted EDA and utilized statistical techniques to analyze the distribution of data from time perspectives.

Implemented feature engineering such as normalization, exploratory data analysis and generated new features with aggregation and cumulated counting functions.

Tracked highly imbalanced Loan dataset using under-sampling, oversampling with SMOTE and cost-sensitive algorithms with Python Scikit-learn.

Developed MapReduce/Spark Python modules for predictive analytics & machine learning in Hadoop on AWS.

Preformed Logistic Regression, Decision Tree and Random Forest algorithms as detection models.

Applied the results of detection models and important features to the second layer of the staking model with the XGboost algorithm; used various metrics such as Precision, Recall, ROC-AUC to evaluate the model result as a guideline of the model improvement process.

Developed and supported enterprise BI solutions, data visualization tools and data warehouse for company's mortgage business.

Generated ad-hoc reports, sub-reports, drill-down reports, drill-through reports and parameterized reports to provide visible data for data analysts and businesses using SSRS and Tableau.

Developed complex SQL queries and created PL/SQL packages on 12 million datasets and automated the workflow process, which increased 20% audience engagement of the platform.

Tuned existing stored procedures and queries by analyzing the execution plans of the query, thus reduced queries running time from 5.00 seconds to 0.68 seconds.

Environment: SQL Server 2012, SQL Server Management Studio (SSMS), SSIS, SQL Server Data Tools (SSDT),), Excel, Visual Studio 2013, Spark (Pyspark, MLlib, Spark SQL), Hadoop 2.x, MapReduce, HDFS, Hive

SinoLink Securities Chengdu, China

SQL Developer May 2014 - Jun 2015

SinoLink Securities is a leading securities and investment management firm that provides comprehensive, tailored solutions and access to clients. The project is to design and develop a reporting pipeline including utilizing SSIS for the ETL process, analyzing data with SAS and generating reports using SSRS to fulfill data solution requirements from business departments. The Tableau reports successfully deliver visualized risk management information to the entire company and external clients. The project involves designing, generating, publishing, and maintaining visualized reports using Tableau, creating database structure, and migrating data for Tableau reports.

Responsibilities:

Designed SSIS packages to extract, transform and load customer data into SQL Server, using Derived Columns, Condition Split, Aggregate, Execute SQL Task, Data Flow Task and Execute Package Task.

Conducted data processing with SQL to generate insights from stock data & financial product data and increased data extraction & manipulation efficiency by 12% by building scripts to extract, process & format data from My SQL Database.

Maintained and developed complex SQL queries, stored procedures, views, functions, and reports that meet customer requirements using Microsoft SQL Server 2012.

Created Views and Table-valued Functions, Common Table Expression (CTE), joins complex subqueries to provide the reporting solutions.

Optimized the performance of queries with modification in T-SQL queries, removed the unnecessary columns and redundant data, normalized tables, established joins and created an index.

Migrated data from SAS environment to SQL Server 2008 via SQL Integration Services (SSIS).

Delivered systematic analytical reports supporting the sales team based on UI dashboards visualization created with Tableau.

Analyzed customer behavior for different financial products and reported portfolio reporting and provided ad-hoc analysis for upper management to drive critical business decisions.

Developed and implemented several types of Financial Reports (Income Statement, Profit & Loss Statement, EBIT, ROIC Reports) by using SSRS.

Generated parameterized dynamic performance Reports (Gross Margin, Revenue based on geographic regions, Profitability based on web sales and smartphone app sales).

Recommended financial products (bond, stock, and treasury) to clients based on fundamental data & quantitative research and increased customer satisfaction rate by 8%, and 73% of customers increased earnings after buying recommended stocks.

Environment: SQL Server 2012 R2, DB2, Oracle, SQL Server Management Studio, SAS/ BASE, SAS/SQL, SAS/Enterprise Guide, MS BI Suite (SSIS/SSRS), T-SQL, SharePoint 2010, Visual Studio 2010, Agile/SCRUM

Education

MS in Information Systems, Fordham University, NY

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