Data Engineer Engineering

Revanth

Sr. Data Engineer

Contact no - +1-320-***-****

email id: *****************@*****.***

Profile Summary

• Around 10+ years of experience in Data Engineering, Big Data/ETL solution development, Data Visualization & Reporting

• Extensively worked on AWS services like EC2, S3, EMR, Sage Maker, RDS(Aurora), Redshift, DynamoDB, Elastic Cache (Memcached & Redis) & Quick Sight, Databricks, Athena, Glue and other services of the AWS family

• Extensive knowledge in working with Azure cloud platforms (HDInsight, Data Lake, Data Bricks, Blob Storage, Data Factory (ADF), Azure Synapse, Azure Analytics, SQL DB, Data Storage Explorer, Azure Databricks

• Extensive experience in working with NoSQL databases and integration with Dynamo DB, Cosmo DB, Mongo DB, Cassandra, and HBase

• Experienced in Designing, Developing, Documenting, and Testing ETL jobs and mappings in Server and Parallel jobs using Data Stage to populate tables in Data Warehouse and Data marts

• Hands-on experience in using Hadoop ecosystem components like Hadoop, Hive, Pig, Sqoop, HBase, Cassandra, Spark Streaming, Spark SQL, Zookeeper, Kafka, Flume, MapReduce framework, Yarn, Scala, and Hue

• Experience with cloud technologies like AWS Step Functions, AWS Lambda, Quick Sight, Cloud Watch, Glue, Athena, Redshift, IAM, EMR, IAM, SNS

• Experience in ETL/ELT ingesting data from various sources into Data Warehouses using business transformations and star/snowflake data modeling

• Experience in data processing like collecting, aggregating, and moving data from various sources using AWS Glue creating Glue crawler jobs, Glue Catalogues, and Python scripts

• Experience in developing CI/CD (continuous integration and continuous deployment) and automation using Git and Kubernetes

• Expertise in Data Migration, Data Profiling, Data Cleansing, Transformation, Integration, Data Import, and Data Export using multiple ETL tools such as Informatica Power Centre, SSIS

• Extensively used Python pytest, pyodbc, NumPy, Pandas, MySQL dB, sqlite3, snowflake-python-connector, and other packages

• Developed various shell scripts, Bash, Scala scripts, and Python scripts to automate Spark jobs and Hive jobs

• Strong Knowledge of the Architecture of Spark distributed and parallel processing using Spark SQL, Spark Data frames APIs, and Spark execution framework

• Experience in OLAP, OLTP, Business Intelligence, and Data Warehousing with emphasis on ETL and Business Reporting needs

• Worked on Key Performance Indicators KPIs, design of star schema, and snowflake schema in Analysis Services (SSAS)

• Designed and implemented data warehouses and data marts using conformed Facts & Dimensions, Slowly Changing Dimensions (SCD), change data capture (CDC), Surrogate Keys, Star, and Snowflake Schema

• Experience in Performance Tuning and Debugging of existing ETL processes using Informatica tools

• Experience in creating Views, clustered and non-clustered Indexes, Stored Procedures, Triggers, Window functions, aggregate functions, and Table Value Functions using DDL, DML and PL/T-SQL

• Experience in converting SQL queries into Spark Transformations using Spark RDDs, Spark SQL, Spark Data frames, and developing python objects in the OOPS programming paradigm

• Experience in working with CSV, text, Excel, parquet, Jason formats of data, and data from RESTful API

• Experience with Requests, NumPy, SciPy, Matplotlib, HTTPLib2, Urllib2, Beautiful Soup, Data Frame, and pandas Python libraries during the development lifecycle

• Experience in predictive modeling, A/B testing, hypothesis testing, ANOVA & Time series analysis (Exponential Smoothing, AR, MA, ARIMA)

• Skilled in correlation and multivariate analysis, causal analytics, and advanced Regression Modelling using statistical modeling concepts

• Experience in analyzing the data using MS Excel (VBA, V - lookup, H – lookup, pivot tables, what-if analysis, scenarios, link cells, charts) and MS Access

• Good understanding of Machine learning algorithms (Deep Neural Networks, Decision Trees, Linear & Logistic Regression, Random Forest, SVM, Bayesian statistics, XG Boost, K-Nearest Neighbors, Natural Language Processing, NLP)

• Managed multiple tasks and worked under tight deadlines and in a fast-paced environment

• Team player, quick learner, organized and self-motivated

TECHNICAL SKILLS

Languages

SQL (Stored Procedures, User Defined Functions, Window Functions, Triggers, Views, Aggregated Functions, Table-valued Functions)

Python (Pandas, NumPy, Scikit Learn, OOPS, Functional Programming)

Python Scripting, SQL, Advanced Excel

Scala, Scala Scripting, Shell Scripting

Version Control

GIT, GIT HUB, GIT Lab, SVN, CVS, Azure Repositories

IDE & Tools, Design

Visual Studio, CI/CD, Jupiter Notebook, PyCharm, Databricks, Zeppelin

Databases

AWS S3, Azure Blob Storage, MY SQL, MS SQL Server

Operating Systems

Linux, Unix, Mac OS-X, CentOS, Windows 10

Cloud Technologies

AWS: AWS Sage Maker, AWS Athena, AWS Glue, AWS EMR, AWS RDS, Lambda, AWS CloudWatch, AWS DMS, AWS Pipeline, S3, EC2, AWS Athena, AWS Glue, MWAA (Apache Airflow)

AZURE: Azure Data Factory (ADF), Azure Data Bricks, Azure Data Lake and Azure Functions, Azure Blob Storage, Azure Runtimes, ETL, ELT

Machine Learning Algorithms

Supervised (Multiple Linear Regression, Logistic Regression, SVM, Stochastic Gradient Techniques, Decision Trees, Random Forest, Naïve Bays Algorithm)

Unsupervised (KNN, K-Means Clustering, Principal Component Analysis, t-SNE, LDA)

ETL/ Reporting

AWS Quick Sight, Tableau, Power BI, Matplotlib, Seaborn, SSIS, SSRS, SSAS, Azure Data Factory(ADF), Databriks

Big Data Ecosystem

HDFS, MapReduce, Hive, Sqoop, Flume, Apache Spark, HBase,

Impala, Oozie, Spark, Zookeeper and Cloudera Manager, Kafka,

Hadoop

Professional Experience

Client: American Express

Designation Data Engineer II Oct 2024 – Present

• Architected a scalable data migration framework leveraging GCP's serverless architecture, implementing parallel processing techniques in DataProc clusters that reduced ETL processing time by 40% while handling terabyte-scale datasets with optimized resource allocation.

• Engineered custom PySpark RDD transformations and DataFrame optimizations utilizing advanced techniques like broadcast variables and accumulators, achieving partition tuning that improved job completion times by 35% for complex aggregation operations.

• Implemented advanced data validation frameworks using Great Expectations with custom PySpark validators, establishing data quality metrics through schema validation, referential integrity checks, and statistical anomaly detection with automated threshold monitoring.

• Developed a comprehensive metadata management system using BigQuery and Cloud Composer that tracked data lineage, processing statistics, and quality metrics, enabling automated impact analysis for upstream data changes.

• Created parameterized Airflow DAGs with dynamic task generation, implementing XCom for inter-task communication and custom sensors for external dependencies while maintaining idempotent operators to ensure reliable reprocessing capabilities.

• Established a robust CI/CD framework with GitHub Actions that included multi-stage pipelines for code validation, automated unit and integration testing with 90%+ coverage, and promotion across E1-E3 environments using infrastructure-as-code principles.

• Implemented custom GitHub Action workflows for automated code quality checks using Black, Flake8, and Pylint, with pre-commit hooks for PySpark optimization patterns and required review approvals through branch protection rules.

• Designed a performance monitoring system using Cloud Monitoring and Logging with custom dashboards that tracked key metrics including processing latency, data throughput, error rates, and resource utilization with automated alerting thresholds.

• Implemented advanced security controls including VPC Service Controls, column-level security in BigQuery, and automated secret rotation with Secret Manager integration, ensuring compliance with data governance policies while maintaining least-privilege principles.

• Developed a disaster recovery strategy with automated backup processes and point-in-time recovery capabilities, implementing multi-region redundancy with documented recovery time objectives (RTOs) and recovery point objectives (RPOs) aligned to business requirements.

• Created a custom framework for handling schema evolution and data versioning that preserved historical data structures while enabling zero-downtime schema changes using BigQuery's schema auto-detection and custom transformation logic.

• Applied machine learning techniques for data quality anomaly detection using AutoML Tables, identifying patterns of data degradation before they impacted downstream systems and creating a self-learning validation system.

• Designed a comprehensive cost optimization strategy that implemented auto-scaling policies for DataProc clusters, BigQuery slot reservations during peak processing times, and automated resource cleanup processes that reduced monthly cloud spending by 25%.

• Developed a GitOps approach to infrastructure management using Terraform modules with parameterized configurations across environments, creating infrastructure testing frameworks that validated expected configurations before deployment.

Environment:

GCP Environments: Dataproc, GCS (Google Cloud Storage), Cloud Functions, BigQuery, Cloud Shell SDK, Google Data Catalog, Cloud Dataflow, Apache Beam, Cloud ML; Other Environments: Oracle, Power BI, Airflow, Hadoop, Scala, Hive SQL, Pandas, Spark, ML models, Pig, Sqoop, Apache Spark, Cloudera Distribution

Client: Mayo Health Clinic, MN Aug 2023- Sep 2024 Designation: Senior Data Engineer

Responsibilities:

• Extracted data from various heterogeneous data sources such as flat files, APIs, databases, and S3 objects

storage using AWS Glue, Py Spark and Databricks

• Work extensively with flat files. Loading them into on-premises applications and retrieving data from applications to files.

• Maintained data pipeline uptime for 99.7% while ingesting transactional and streaming data across 5 different data sources using Spark, Redshift, AWS S3, and python.

• Integrated workflow using AWS lambda, AWS Event Bridge, and AWS step functions to create a triggered data pipeline for event-based data loads from S3 to Redshift

• Implemented Python scripts using Py Spark, Databricks, AWS Glue, and Lambda functions to create on-demand processing on S3 files

• Analyzed and processed the S3 data from starting stage to the persistence stage by using AWS Athena, Glue crawlers, and creating glue jobs

• Managed and optimized job scheduling and batch processing using Control-M to ensure timely execution of data pipelines.

• Automated workflow monitoring and alerting for task failures and retries, enhancing pipeline reliability and uptime.

• Introduced cloud-based technologies into Python development to expand on-premises deployment options

• Automated infrastructure deployment and scaling to support continuous integration and delivery processes.

• Implemented monitoring, logging, and alerting systems to ensure high availability and performance of applications.

• Proficient in implementing event-driven architectures using Apache Kafka, facilitating real-time data streaming and message processing.

• Configured and managed Elastic Load Balancers (ELB) to distribute incoming application traffic across multiple EC2 instances for enhanced scalability and fault tolerance.

• Designed and maintained relational databases using Amazon RDS (Relational Database Service), ensuring data integrity, high availability, and efficient query performance.

• Managed DNS routing and domain management with Amazon Route 53, providing scalable and reliable access to applications and services.

• Implemented Virtual Private Cloud (VPC) configurations, including subnets, route tables, and network ACLs, to isolate and secure AWS resources.

• Established secure connections between on-premises networks and AWS VPCs using VPN (Virtual Private Network) solutions for secure data transmission.

• Orchestrated network connectivity and routing between multiple VPCs using AWS Transit Gateway (TGW), simplifying network management and enabling scalable inter-VPC communication.

• Leveraged AWS Managed Streaming for Apache Kafka (MSK) to build scalable, durable, and highly available streaming data pipelines.

• Extracted data from heterogeneous sources and transformed it into actionable insights using AWS Glue, PySpark, and Databricks.

• Ensured high availability and reliability of data pipelines, achieving 99.7% uptime across multiple data sources using Spark, Redshift, AWS S3, and Python.

• Implemented serverless data processing workflows using AWS Lambda, EventBridge, and Step Functions for event-driven data ingestion and processing.

• Developed Python scripts and Lambda functions for on-demand processing of data stored in AWS S3, enabling efficient data manipulation and analysis.

• Used Python, and Databricks to write AWS Lambda function for nested JSON files and sorted, compared, and converted to Parque format

• Developed API Gateways, to submit data via API Gateway that is accessible via Lambda functions

• Used AWS Redshift Spectrum, and Athena services to query the large amount of data stored on S3

using the Glue data catalog

• Identified and resolved performance bottlenecks in data pipelines and cloud infrastructure, improving processing efficiency by 30%.

• Designed, deployed, and maintained scalable cloud infrastructure using AWS services like EC2, S3, Redshift, and Lambda.

• Implemented multi-tier cloud architectures and automated deployments, improving system reliability and cost efficiency.

• Conducted root cause analysis and optimized SQL queries, Spark jobs, and resource allocation for faster execution.

• Created AWS Step Functions using python for deployment management in AWS and designed, investigated, and implemented public-facing websites on Amazon Web Services and integrated it with other applications’ infrastructure

• Implemented AWS services like EC2, SQS, SNS, IAM, S3, and Dynamo DB to deploy multi-tier applications concerning fault tolerance, high availability, and auto-scaling in AWS Cloud formation

• Used EMR PySpark jobs to transform and move large amounts of data into and out of databases, such

as Amazon S3 and DynamoDB

• Integrated Apache Airflow with AWS to monitor multi-stage workflows with the tasks running on Amazon Sage Maker

• Presented technical solutions to stakeholders and documented complex processes for both technical and non-technical teams.

• Facilitated knowledge sharing through clear and concise documentation of workflows, best practices, and project updates.

• Used Athena federated queries using the Athena Data Source Connectors & AWS Lambda to query data

from multiple heterogeneous sources

• Developed Spark jobs on Databricks to perform tasks like data cleansing, data validation, and standardization, and then applied transformations as per the use cases

• Created Hive-compatible tables schemas on top of raw data in Data Lake which were partitioned by time dimension key, and product dimension, and then analyzed, ad-hoc queried using AWS Athena

• Implemented Spark context, Spark-SQL, Data Frames, and pair RDDs to optimize the existing algorithm in Hadoop

• Handled streaming data from the web server console log and used the spark with SQS to stream the data in real-time

• Experienced in using Jenkins and working with GIT Version Control System and Kubernetes

• Worked on Agile (Scrum) Methodology, participated in daily scrum meetings, and was actively involved in sprint planning and product backlog creation

• Utilized SDLC and Agile methodologies such as SCRUM

Environment:

Spark, Spark-Streaming, Spark SQL, AWS EMR, S3, Lambda, EC2, Redshift, RDS, Glue, Linux, Shell Scripting, Python, PySpark, HDFS, MapReduce, Hive, PIG, Apache Kafka, Sqoop, MySQL, GIT, Kubernetes, Oozie, Cassandra, and Agile Methodologies, Apache Airflow (MWAA)

Client AT&T, TX

Designation: Data Engineer May 2022 – Jul 2023

Responsibilities:

• Worked on Azure cloud platform services like HDInsight, Data Lake, Data Bricks, Blob Storage, Azure Data Factory (ADF), Azure Synapse, and Data Storage Explorer

• Ingested data to one or more Azure Services - (Azure Data Lake, Azure Blob Storage, Azure SQL DW) and processed the data using Azure Databricks and Azure Analytics services

• Developed data ingestion pipelines on Azure HDInsight Spark cluster using Azure Data Factory (ADF) and Spark SQL and persisted into Azure Synapse analytics

• Designed and managed cloud infrastructure using EC2, S3, Lambda, and Redshift for scalable, secure applications.

• Configured VPCs, load balancers, and auto-scaling groups to optimize resource allocation and fault tolerance.

• Designed and deployed data pipelines using Azure Data Lake, Data Bricks, and Apache Airflow

• Created data pipeline for different events in Azure Blob storage into Hive external tables and used various Hive optimization techniques like partitioning, bucketing, and Map join

• Worked on Azure Data Factory (ADF) to integrate data of both on-prem (PostgreSQL, Cassandra) and cloud (Blob Storage, Azure SQL DB) and applied transformations to load back to Azure Synapse

• Wrote Python scripts,Bash Scripts to build ETL pipelines and Directed Acyclic Graph (DAG) workflows using Apache Airflow, and Apache NiFi

• Migration of on-premises data (Oracle/ SQL Server/ DB2/ MongoDB) to Azure Data Lake and Stored into ADLS using Azure Data Factory (ADF V1/V2).

• Designed custom-built input adapters using Spark, and Hive to ingest and analyze data (Snowflake, MS SQL, MongoDB) into HDFS

• Developed Spark applications using PySpark and Spark-SQL for data extraction, transformation, and aggregation from multiple file formats for analyzing & transforming the data

• Ingested data in mini-batches and performs RDD transformations on mini-batches of data by using Spark Streaming to perform streaming analytics in Azure Databricks

• Used Azure DevOps and VSTS (Visual Studio Team Services) for CI/CD, Active Directory for authentication

• Worked on Azure Data Factory to integrate data of both on-prem (MY SQL Cassandra) and cloud (Blob storage, Azure SQL DB) and applied transformations to load back to Azure Synapse

• Managed resources and scheduling across the cluster using Azure Kubernetes Service (AKS). AKS has been used to create, configure, and manage a cluster of virtual machines

• Applied transformations to load back to Azure Synapse. To meet specific business requirements wrote UDFs in Scala and Pyspark

• Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the SQL Activity

Environment:

Azure Data Factory (ADF), Blob Storage, Synapse, Azure SQL, Azure HDInsight, Databricks, DataLakeGen2, Cosmos DB, AKS, MySQL, PostgreSQL, Snowflake, MongoDB, Cassandra, Teradata, Terraform, Python, Spark, RDD, PySpark, Airflow, DAG, Hive, Sqoop, HBase, AKS

Client: Fiserv, GA

Designation: Data Engineer Jan 2021– April 2022

Responsibilities:

• Involved in creating data ingestion pipelines for collecting data from various external sources like FTP Servers and AWS S3 buckets

• Designed and developed Security Framework to provide fine-grained access to objects in AWS S3 using AWS Lambda, and DynamoDB

• Used AWS EMR to transform and move large amounts of data into and out of other AWS data stores and databases, such as AWS S3 and Amazon DynamoDB

• Involved in migrating existing traditional ETL jobs to Spark and Hive Jobs on the new cloud S3 data lake

• Implemented Columnar Data Storage, Advanced Compression (snappy), and Massive Parallel Processing using AWS Redshift

• Responsible for maintaining quality reference data in source by performing operations such as cleaning, transformation, and ensuring Integrity in a relational environment by working closely with the stakeholders & business manager

• Automated provisioning of AWS resources using Infrastructure-as-Code (IaC) with Terraform, ensuring consistency across environments.

• Managed state files and created reusable modules for efficient resource management and version control.

• Worked on the code transfer of a quality monitoring program from AWS EC2 to AWS Lambda, as well as the creation of logical datasets to administrate quality monitoring on Snowflake warehouses

• Utilized AWS Athena and Glue Crawlers to analyze and process data stored in Amazon S3, enabling ad-hoc querying and exploration of large datasets.

• Integrated cloud-based technologies into Python development workflows, expanding deployment options and scalability.

• Designed and deployed API Gateways to enable secure and scalable access to Lambda functions for data submission and processing.

• Leveraged AWS Redshift Spectrum and Athena to query large datasets stored in Amazon S3, utilizing Glue Data Catalog for schema management.

• Orchestrated deployment management using AWS Step Functions and Python, ensuring seamless application deployment and management in AWS.

• Utilized AWS services such as EC2, SQS, SNS, IAM, S3, and DynamoDB to deploy fault-tolerant and scalable multi-tier applications in AWS CloudFormation.

• Integrated Apache Airflow with AWS to monitor and manage multi-stage workflows, with tasks executed on Amazon SageMaker for machine learning workloads.

• Proficient in using Jenkins and GIT Version Control System within Agile (Scrum) Methodology, Kubernetes, contributing to sprint planning and product backlog refinement in SDLC processes.

• Responsible for monitoring and troubleshooting the AWS EMR Spark cluster

• Developed ETL framework using Spark and Hive (including daily runs, error handling, and logging)

• Involved in building a real-time pipeline using Kafka and Spark streaming for delivering event messages to the downstream application team from an external rest-based application

• Worked on Automation and scheduling of UNIX shell scripts and batches using Airflow bash operations

• Worked on analyzing spark cluster and different big data analytic tools such as HiveQL

• Developed Bash,Python and Scala scripts, and UDFs using both Data frames/SQL and RDDs in Spark for Data Aggregation, queries, and writing data back into RDBMS

• Wrote shell scripts to monitor the health check of spark clusters and respond accordingly to any warning or failure conditions

• Used broadcast variables in spark, effective & efficient Joins, caching, and other capabilities for data processing

• Created Partitioned Hive tables (managed and external) and worked on them using HiveQL

Environment:

AWS, Hadoop, Kafka, Airflow (MWAA), Unix, AWS EMR, Dynamo DB, Spark, AWS Lambda, HDFS, SQL, Spark, Terraform,PySpark, Hive, HDFS, Sqoop, Kafka, HBase, Scala

Client Verizon

Designation: ETL Developer Nov 2018 – Dec 2020

Responsibilities:

• Extensively worked on Informatica tools such as Source Analyzer, Mapping Designer, Mapplet Designer, Warehouse Designer, Transformation developer, Informatica Repository Manager, and Informatica Workflow Manager

• Worked as an Informatica/Data Warehouse Developer helping the team with production issues, enhancements, and maintenance of existing code

• Developed automation scripts for data pipelines, cloud orchestration, and real-time data processing using Python.

• Integrated Jenkins and GitLab CI for automated testing, deployment, and continuous delivery to production environments.

• Created end-to-end CI/CD pipelines using Docker, Kubernetes, and AWS CodePipeline to streamline development workflows.

• Built and maintained Python-based Lambda functions for serverless processing, optimizing performance and cost.

• Worked on SQL queries against the data store to validate the accuracy and in helping the testing strategy for the QA team

• Used Package deployment, Import/Export, Configuration modifications, Scheduling, and Backup to instigate and manage Informatica packages

• Responsible for reviewing Informatica ETL designs and working with developers to confirm proper standards are followed

• Implemented parallel execution, removed unnecessary sorting, and used optimized queries to improve the performance of Informatica packages

• Extracted data from different flat files, MS Excel, and MS Access and transformed the data based on user requirements using Informatica Power Center and loaded data into the target, by scheduling the sessions

• Created data dictionary, Data mapping for ETL and application support, ERD, mapping documents, metadata, DDL, and DML as required

• Created different types of reports including financial reports, crosstab, conditional, drill-down, sub-reports also parameterized reports and ad hoc reports for existing databases

• Design and created financial report templates, bar graphs, and pie charts based on the financial data. Scheduled the monthly /weekly/daily reports to run automatically

• Encapsulated frequently executed SQL statements into stored procedures, functions, indexes, and views to reduce the query execution times

• Created SSIS packages to implement error/failure handling with event handlers, row redirects, and loggings

• Created SQL server configurations for SSIS packages and experienced in creating jobs, alerts, and SQL mail agents and scheduling SSIS packages

• Designed, analyzed, and Implemented ETL processes, ETL Testing, ETL standards, and naming conventions and wrote ETL flow documentation for Stage, ODS, and Data Marts

• Used SSIS and T-SQL stored procedures to transfer data from OLTP databases to the staging area and finally transfer it into the data warehouse

• Used various SSIS tasks such as conditional split, derived column, and lookup which were used for data scrubbing, and data validation checks during Staging, and before loading the data into the data warehouse

• Responsible for creating reports based on the requirements using SSRS and defined the report layout and identified datasets for the report generation

Environment:

MS SQL Server, Informatica, Informatica Power Center, ETL, OLAP, OLTP, SQL, SSIS, SSRS, SSAS

Client Zen3, Hyderabad, INDIA

Designation: Reporting Analyst Aug 2014– July 2018

Responsibilities:

• Created reports in Tableau for visualization of the data sets created and tested Spark SQL connectors

• Worked with Tableau and Integrated Hive, Tableau Desktop reports and published to Tableau Server

• Involved in Requirement gathering for business analysis, and translating business requirements into technical design

• Create Tableau interactive dashboards and reports for teams within the organization

• Developed VBA macros programs to automatically update excel workbooks, encompassing class and program modules and external data queries, and linked the based module to enable automatic updates

• Used advanced financial and statistics formulas, user-defined functions, and formulaic arrays for data analysis using advanced excel

• Converted Excel formulas into VBA code, including V - lookup, and H – lookup, and developed excel macros for automation

• Actively involved in User Acceptance Testing (UAT) and Training of the end users. Participated in cross-functional teams to reengineer and improve business processes

• Validated and modeled data with the purpose of understanding or making conclusions from the data for decision-making purposes

• Maintain or updated thousands of contract validity periods, updating, and posting bills receipts, and invoices; ensuring all documents are accurate before issuing credits

• Supported exchange of feedback and communication between management and associates

• Logged reports, contracts, invoices, and sales documents into a digital database with Excel

• Checked all layers of maps to promote accuracy, identifying and marking errors and making corrections

• Compiled, organized, and corrected data with advanced Excel functions

• Participated in meetings and database analysis with Developers, Project Managers, and Quality Analysts

to discuss business requirements, test planning, resource utilization, and defect tracking

• Developed Data Flow Diagrams, illustrating the flow of data from the legacy systems into the application database Tables, along with checkpoints for testing/verification

• Assisted in test planning and execution. Created a test plan for User Acceptance Testing. Developed user

test cases and validated test results during user acceptance testing

• Analyzing data using statistical techniques and providing reports using advanced excel...

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