Nayeem Uddin Email & Phone Number

Memphis, Tn, Us

• Hands in experience in working with Continuous Integration and Deployment (CI/CD) using Jenkins, Docker.• Queried multiple databases like Snowflake, UDB and MySQL for data processing.• Analyzed and processed complex data sets using advanced querying using Presto, HIVE and Teradata, visualization using Tableau and analytics tools such as Python and SAS.• Developed ETL pipelines in and out data warehouse using combination of Python and Snowflake SnowSQL. Writing SQL quires against Snowflake. • Exposure to Full Lifecycle (SDLC) of Data Warehouse projects including Dimensional Data Modeling.• Worked with building data warehouse structures, and creating facts, dimensions, aggregate tables, by dimensional modeling, Star and Snowflake schemas.• Extract Transform and Load data from Sources Systems to Azure Data Storage services using a combination of Azure Data Factory, Spark SQL, and U-SQL Azure Data Lake Analytics. Data Ingestion to one or more Azure Services - (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in In Azure Databricks.• Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the Sql Activity.• Created Sessions and extracted data from various sources, transformed data according to the requirement and loading into data warehouse.• Used various transformations like Filter, Expression, Sequence Generator, Update Strategy, Joiner, Router and Aggregator to create robust mappings in the Informatica Power Center Designer.• Used Informatica Power Center for (ETL) extraction, transformation and loading data from heterogeneous source systems into target database.• Used ER Studio for Creating/Updating Data Models.• Creating Databricks notebooks using SQL, Python and automated notebooks using jobs.• Created mappings using Designer and extracted data from various sources, transformed data according to the requirement.

New York, New York, Us

• Responsible for the design, implementation, and architecture of very large-scale data intelligence solutions around big data platforms.• Analyzed large and critical datasets using HDFS, HBase, Hive, HQL, Pig, Sqoop and Zookeeper.• Developed multiple POC’s using Spark, Scala and deployed on the Yarn Cluster, compared the performance of Spark, with Hive and SQL.• Use Amazon Elastic Cloud Compute (EC2) infrastructure for computational tasks and Simple Storage Service (S3) as storage mechanism. • Capable of using AWS utilities such as EMR, S3 and Cloud Watch to run and monitor Hadoop and Spark jobs on AWS.• Writing Custom pyspark scripts to replicate the function of the Teradata code in the process of data transformation from Teradata to AWS one lake.• Used SAS/ACCESS to extract data from different relational database management ORACLE and SQL SERVER systems using SAS.• Extensively used the SET, UPDATE and MERGE statements for creating, updating, and merging various SAS data sets.• Extracted, transformed, and loaded data using SAS/ETL.• Involved in various Transformation and data cleansing activities using various Control flow and data flow tasks in SSIS packages during data migration• Developed Mappings, Sessions, and Workflows to extract, validate, and transform data according to the business rules using Data Stage.• Worked on developing Kafka Producers and Kafka Consumers for streaming millions of events per second on streaming data. • Implemented a distributing messaging queue to integrate with Cassandra using Apache Kafka.• Hands on experience on fetching the live stream data from UDB into HBase table using PySpark streaming and Apache Kafka.• Worked on Tableau to build customize interactive reports, worksheets, and dashboards.

Bentonville, Arkansas, Us

• Responsible for ingesting large volumes of user behavioral data and customer profile data to Analytics Data store.• Sqoop jobs for ingesting from FTP servers into databases.• Developed Scala based Spark applications for performing data cleansing, event enrichment, data aggregation de-normalization and data preparation needed for machine learning and reporting teams to consume.• Worked on troubleshooting spark application to make them more error tolerant.• Worked on fine-tuning spark applications to improve the overall processing time for the pipelines.• Experienced in handling large datasets using Spark in Memory capabilities, using broadcasts variables in Spark, effective & efficient Joins, transformations and other capabilities.• Experience developing pipelines in GCP and Azure.• Involved in creating Terraform and Teamcity builds.• Developed automation pipelines in ADF, Airflow and Atomic.• Deprecated Oracle and Netezza jobs and transformed the code to the new stack.• Implemented Partitioning, Dynamic Partitions, Buckets in HIVE.• Collaborated with the infrastructure, network, database, application and BA teams to ensure data quality and availability.• Designed, documented operational problems by following standards and procedures using JIRA.• Developed custom multi-threaded python-based jobs

Cincinnati, Ohio, Us

• Executed entire Data Life Cycle and actively involved in all the phases of project life cycle including data acquisition, data cleaning, data engineering, feature scaling, feature engineering and statistical modeling. • Created multiple workbooks, dashboards, and charts using calculated fields, quick table calculations, Custom hierarchies, sets & parameters to meet business needs using Tableau. • Create HIVE queries to join multiple tables of a source system and load them into Elastic Search Tables and used HIVE QL scripts to perform the incremental loads.• Performed Real time event processing of data from multiple servers in the organization using Apache Storm by integrating with Apache Kafka.• Involved in development of full life cycle implementation of ETL using Informatica, Oracle and helped with designing the Date warehouse by defining Facts, Dimensions and relationships between them and applied the Corporate Standards in Naming Conventions.• Involved in complete BigData flow of the application starting from data ingestion from upstream to HDFS, processing the data in HDFS and analyzing the data.• Used SparkR Data frames, Spark-SQL, Spark MLib extensively and developing and designing POC's using Scala, Spark SQL and MLlib libraries.• Created MDM, OLAP data architecture, analytical data marts, and cubes optimized for reporting.• Developed several advanced MapReduce programs in Java as part of functional requirements for Big Data.• Worked with different sources such as Oracle, Teradata, SQLServer2012 and Excel, Flat, ComplexFlat File, Cassandra, MongoDB, HBase, and COBOL files.• Led teams performing Data mining, Data Modeling, Data/Business Analytics, Data Visualization, Data Governance & Operations, and Business Intelligence (BI) Analysis and communicated insights and results to the stake holders.

New York, New York, Us

• Data Modeling and Cube Partitioning in SSAS and enhanced the functionality in database and data ware housing concepts (OLAP, OLTP) Cube by creating KPI, Actions using SSAS• Analyzed various assignments that improve decision-making and generate financial reports using the latest analytics tools such as Tableau.• Created and Designed Data Source and Data Source Views and configured OLAP Cubes (Star and Snow Flake Schema) using SSAS.• Creating SSIS Packages by using different data Transformations like Derived Column, Lookup, Data Conversion, Conditional Split, Pivot, Union all and Execute SQL Task to load data into Database(T-SQL).• Designing and Deployment of Reports for the End-User requests using Web Interface & SSRS.• Experience in Spark Streaming using Scala from real time queue for streaming data and created RDDs in Spark using Spark Context and used Scala APIs to read multiple data formats.• Worked in writing SPARK SQL scripts for optimizing the query performance. Importing the data from Netezza onto out HDFS cluster.• Used Pig as ETL tool to do transformations, joins and some pre-aggregations before storing the data into HDF.• Developed MapReduce programs to cleanse the data in HDFS obtained from heterogeneous data sources to make it suitable for ingestion into Hive schema for analysis.• Responsible for building scalable distributed data solutions using Hadoop cluster environment with Load the data into Spark RDD and performed in-memory data computation to generate the output response.