SUMMARY:

• 6+ Years of professional Experience with a master’s degree in operations research which aided me in using IT and Analytic applications ranging from relational databases to not only SQL and big data warehousing to using SAS/R/Python/Spark/Hadoop for advanced analytics and reporting.
• Experienced to apply data driven statistical methods and operations research techniques for making the right business decision making.
• Highly experienced in developing data marts and warehousing with advanced transformation for ETL (Extract, Transform & Load Process) using SQL, Presto Sql, HiveSql, SAS, Python(Pandas, Numpy) and Pyspark.
• End to end IT & statistical analytics knowledge of converting raw big data sources into useful business insights using advanced SAS /R/Python programming in Hadoop, Spark & UNIX environments.
• Developed advanced SAS visual Analytics reporting using almost all the advanced features available on SAS VA 7.3, have moderate experience in using tableau and Power BI.
• Good business knowledge working in manufacturing, supply chain and healthcare domains.
• Experience in driving Reliability and Asset Management programs in maintenance and production departments using advanced charts and statistics programmed using SAS/SQL.
• Experience in handling and driving big data ETL projects using HIVEQL, Presto Sql connections and SAS.
• Experience in handling huge amounts of code transformation when done from relational database programming to Hadoop databases.
• Advanced SAS Programming 9, certified (Scored 92% in the examination).
• Experience working in SAS Grid environments (High Performance Analytics Suite) and SAS Unix for automation.
• Skilled in working with relational databases (SAS Access to Oracle, MS - SQL Server, Teradata) and their integrations with SAS and R for analytics.
• Used various techniques from SAS/Base, SAS/Advanced for descriptive analytics and statistical analysis using SAS/STAT.
• Hands on project experience with the following tools SAS Predictive Maintenance, SAS/Visual Analytics, and SAS Enterprise Miner for predictive Analytics for clustering, classification.
• Experience in working on Hortonworks Hadoop platform on both on premise and Google cloud environments. Hand on experience in migrating the Hive codes between the environments.
• Hands on experience in using the latest Hive version (which is LLAP) and presto SQL

TECHNICAL SKILLS:

• Python
• SAS
• Bash Scripting
• HiveSql
• SQL
• Core Java
• R Studio statistics
• Base SAS
• SAS/Operations Research
• SAS Enterprise Miner
• Python
• Auto Cad
• IBM predictive and preventive maintenance
• Reliasoft(RCM++
• Weibull)
• SQL
• MATLAB
• Core JAVA
• R Shiny
• Innoslate (Systems Requirements Analysis Tool)
• Strong background in EXCEL and Excel Data analysis: Forecasting.

PROFESSIONAL EXPERIENCE:

Confidential, Houston, TX

Sr. Data Engineer / Sr. SAS Developer

Responsibilities:

• Converting old oracle SQL/PL- SQL, Microsoft SQL server/ T- SQL written SQL to run on big data platforms using PYSPARK, SPARKSQL and HIVE with SAS and Python as programming platforms.
• Developing the data pipelines on Hadoop using SAS or Pyspark (Spark 2.2 and Sparkl Sql)
• We use Google Cloud Platform for compute (IAAS).
• Building fully automated shell scripts for SQOOP imports/exports, moving data across different Hadoop networks etc.
• Coding, testing, debugging, documenting and maintaining SAS and python programs.
• Converting SAS programs to python based for efficiency and utilizing the packages from NUMPY and PANDAS.
• Optimizing the Hive code and utilizing the more advanced hive sessions. (currently Hive 2.0 LLAP)
• Extracting data from databases using SAS SQL procedures and Spark Core libraries to manipulate, aggregating and merging datasets.
• Designing reports and developing insightful visualizations using SAS Visual Analytics and Power BI.
• Collaborating with system integration and data warehouse engineers on data extraction and data cleansing and providing the required to data scientists.
• Extensive model building using linear regression, survival and logistic regression in SAS visual statistics platform building predictive models for estimating the Assets & Products consumption.
• Building scenario analysis using time series plots in SAS Visual Analytics.

Confidential, Minneapolis, MN

Sr. SAS Programmer (Data Management)

Responsibilities:

• Moving all the traditional PC SAS programs into high performance analytics grid to ease the use of advanced BI tools like SAS visual analytics, Web reporting, Information maps studio etc.
• Major responsibilities were with data preprocessing, data pulling and building predictive models using SAS Enterprise miner.
• Experience in connecting to SAS interface to Hadoop and querying results to SAS BI client tools.
• Moving 30000 programs written and run on PC SAS to Grid computing platforms depending upon the user based customization, which runs on UNIX.
• Visual data exploration using SAS visual Analytics connecting to in memory LASR analytic server to quickly determine relationships across hundreds of parameters in millions of rows of data.
• Developing and maintaining SQL based database that interfaces with both SAS to automate history archiving, retrieval, and forecast generation
• Integrating Visual Basic code with SAS for reporting purposes in excel using SAS add-in for Microsoft products and Dynamic data exchange.
• Creating large datasets by combining individual datasets by automating the code, which pulls data from various databases in one single data step for big data analysis.
• Experience in working with data validation checks, data cleansing data imputation techniques using efficient data step and macro facility.
• Analyzing data using various statistical PROCEDURES like PROC SUMMARY, PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC REG, PROC LP(SAS OR) and PROC ANOVA.
• Experience in using logistic regression, survival analysis and various high performance analytics procedures used in high performance analytics suite.
• Worked on Data preparation for time series and transactional data along with the data mining techniques such as similarity analysis and cluster analysis of the large data.

Environment: Base/ SAS, SAS/Macro, SAS/SQL SAS/Connect, SAS/Graph, SAS/Access, VDI/SAS, Windows, SAS GRID, Teradata, DB2, UNIX & SAS/ENTERPRISE Miner, Guide, SAS BI tools, OLAP studio, Information Maps, SAS Visual Analytics

Confidential

Senior Reliability Data Engineer

Responsibilities:

• Primary role was to identify and manage asset reliability risks that could adversely affect plant or business operations. Worked on Loss Elimination, Risk Management and Life Cycle Asset Management using SAP -Plant maintenance module.
• Use Base SAS for cleaning the data from IBM Maximo and analyzing the historical data of maintenance for dashboard analysis and indicators to drive the continuous process management.
• Created the indicators necessary to track the progress on a continuous improvement strategy for maintenance, quality and production teams and managed the results using SAS/STAT, SAS/Graph and R for visualizations.
• Compiling, reviewing data and developing equipment or skid specific action plans for critical assets to improve reliability and reduce production costs.
• Developed lead metrics and tracks metrics on assets and other KPI’s.
• Developed and managed predictive maintenance program with the state of the art predictive maintenance techniques.

Environment: Reliasoft (RCM++, Weibull) SAP - Plant Maintenance, D3.js, SAS/STAT, SAS/BASE, R. IBM Maximo, Allen Bradley Automation, Rotating and Static mechanical Equipment, Predictive Maintenance Models, Microsoft SQL Server Database, Gantt Charts, Reliability Software’s, FMEA, FRACAS.

Confidential

Machine Reliability Data Analyst

Responsibilities:

• He had been trained in the Michelin IT School and Michelin Quality School and was working hands on in one of the Michelin facilities in the areas of Overall Equipment effectiveness and predictive analytic for identifying part failures, devising strategies to lower the maintenance and production costs using data analytics with SAS
• Used Economic Order Quantities supply chain Model for determining the order quantities of the maintenance spare parts, saved up-to 200,000USD per annum with SAS/OR techniques.
• Developed intensive domain knowledge in the functional areas like supply chains, quality, and maintenance and production operations.