Data Engineer

VANDANA GOUD SURA

BIG DATA ENGINEER

*************************@*****.*** +1-774-***-****

PROFESSIONAL SUMMARY

Analytic Data Engineer with 9 years of proven experience in utilizing Big Data technologies to manage various aspects of master data management, including data ingestion, modeling, querying, processing, analysis, and implementing enterprise-level systems focused on Big Data and Data Integration.

Extensive expertise in Big Data technologies, particularly within the Hadoop and Spark ecosystems. Proficient in a wide range of tools and technologies, including Apache Spark, Kafka, Storm, Hive, Machine Learning, Cassandra, MongoDB, Pig, Elasticsearch, Sqoop, HBase, Tableau, MapReduce, IoT, Flume, Oozie, HDFS, Yarn, Hadoop Common, Spark Core, Spark SQL, Spark Streaming, MLlib, GraphX, Spark R, Artificial Intelligence, Drill, Data Mining, Data Visualization, Flink, Predictive Analytics, and Presto.

Hands-on experience working with leading Hadoop and Spark distribution platforms such as Cloudera, Hortonworks, IBM BigInsights, Databricks, CDH, HDP, MapR, IBM Analytics Engine, EMR, DataProc, and Qubole. Additionally, proficient in utilizing cloud platforms like AWS and GCP to perform Big Data processing, analysis, and solution implementation.

Solid understanding and practical experience with a broad range of AWS and GCP services, including Amazon EC2, EKS, S3, QuickSight, Elasticsearch, RDS, ELB, DynamoDB, MSK, Snowflake, SageMaker, Redshift, CloudTrail, IAM, Auto Scaling, CloudWatch, CloudFormation, SNS, SQS, Athena, Glue, Kinesis, Lambda, EMR, KMS, Elastic Beanstalk, Route 53, NACL, Route Tables, VPC, Subnets, EBS, API Gateway, CloudFront, IoT, Cognito, CloudSearch, EFS, EventBridge, Fraud Detector, SES, Backup, along with GCP services like BigQuery, Functions, Firestore, DataProc, GCS, GCE, Bigtable, Datastore, Memorystore, App Engine, Spanner, SQL, GKE, Dataflow, Search, Workspace, GC Engine, CDN, DNS, Firewall, Load Balancing, NAT, Router, VPN, Service Directory, Data Catalog, Vision, Translation, Natural Language APIs, Dataform, Data Fusion, Datalab, Datastream, Pub/Sub, and ML Engine. These services drive increased agility, scalability, cost savings, collaboration, and optimized data processing and analytics.

Proficient in performing data processing tasks such as collecting, aggregating, and transferring data from various sources using Apache Flume and Kafka.

Skilled in designing and implementing Python-based REST APIs for large-scale applications.

Architected a data pipeline using Kafka and Spark Streaming to store data in HDFS, enabling real-time analytics on streaming data.

Expertise in leveraging Apache Spark’s job execution framework, including the Driver, Spark Context, Cluster Manager, Tasks, DAG Scheduler, Stage, Task Scheduler, and Executors.

Developed Spark applications for production environments using Spark RDD APIs, DataFrames, Spark SQL, and Spark Streaming APIs.

Successfully collaborated with global teams in Europe, Mexico, and India using agile methodologies, fostering effective and productive cross-regional cooperation.

Proficient in using Power BI and Tableau to import data from multiple sources, generating insightful reports for data-driven analysis, business insights, and informed decision-making.

CERTIFICATIONS:

AWS Certified Solutions Architect.

EDUCATION

Bachelor of Technology: Jawaharlal Nehru Technological University Hyderabad, India

Electrical and Electronics Engineering (EEE)

TECHNOLOGY

Hadoop & Spark Distribution Platforms

Cloudera, Hortonworks, IBM BigInsights, Databricks, CDH, HDP, MapR, IBM Analytics Engine, EMR, Data Proc & Qubole.

Programming Languages

SQL, Python, Scala & Shell Scripting.

Cloud Platform

AWS & GCP.

Reporting Tools

Tableau & Power BI.

Deployment Tools

Jenkins & UrbanCode Deploy.

Designing Tools

UML & MS Visio.

IDE Tools & Utilities

Jupyter Notebook / Lab, CLI, SQL Editors, Visual Studio Code, Eclipse, IntelliJ IDEA, PyCharm, Apache Hadoop Tools like Hive, Airflow, & Zeppelin.

ETL Tools

MS SSIS, DataStage, ODI, OMOP CDM, Apache Spark & Kafka.

Web Technologies

XML, HTML & CSS.

Databases

Oracle, MS SQL Server, MYSQL, Teradata, Exadata, Cassandra, MongoDB, HBase, Amazon Dynamo DB, Redshift & Elasticsearch.

Big Data Technologies

Apache Spark, Kafka, Strom, Hive, ML, Cassandra, MongoDB, Pig, Elasticsearch, Sqoop, HBase, Tableau, MapReduce, IoT, Flume, Oozie, AI, Drill, Data mining, Disaster Recovery, Data Visualization, Flink, Predictive Analytics, and Presto.

Ticketing Tools

JIRA, Service Now & MS Dynamics 365 Customer Service.

Version Control Tools

Git, Bitbucket, CVS, Subversion, and SourceAnywhere in Tracking, Managing, and Organizing changes made to files and code over time.

Operating Systems

Linux (Ubuntu, Unix, Red Hat Enterprise & CentOS) & Windows.

Scheduling Tools

Control-M, MS SQL Server Agent, Kubernetes, IBM Tivoli Workload Scheduler, Apache Airflow, & Oozie.

Implementation Methodologies

Agile, Waterfall & DevOps.

Others

Putty, WinSCP, Data Lake, Data Marts, ODS, MS Office, RTC, OPTIM, IGC, WinSCP, Data Vault, CLI, Splunk, SSH, TDM, Docker & Text Editors.

Client: XponentL, Los Angeles, CA Jul 2024 - Present

Role: Data Innovation, Management, & BI

Responsibilities:

Managing data migration pipelines using AWS Redshift to ensure seamless and efficient data transfer.

Design, develop, and maintain scalable and reliable data pipelines to ingest, process, and transform data from various sources.

Developed and optimized real-time data pipelines using GenAI for scalable, low-latency processing and advanced analytics.

Designed and implemented robust Master Data Management (MDM) solutions for integrating and managing healthcare provider (HCP) and healthcare organization (HCO) data.

Collaborate with data scientists, ML engineers, and NLP experts to understand their data requirements and develop appropriate data engineering solutions.

Implemented data ingestion workflows in Databricks using Apache Spark, integrating with AWS S3 and Redshift for seamless data storage and retrieval.

Implement and maintain ETL processes to map source data from Oracle to the OMOP CDM structure, ensuring high data quality and optimal performance.

Develop, test, and maintain APIs to expose data and services for internal and external consumption.

Developed real-time data pipelines for Cyber AI applications, enabling proactive threat detection and anomaly analysis across large-scale network environments.

Automated vulnerability assessments to identify risks early in the SDLC.

Enhanced compliance by embedding security policies in DevOps workflows.

Integrated machine learning algorithms into real-time data workflows, enhancing predictive capabilities for identifying security vulnerabilities.

Leverage Python as the core programming language for data engineering tasks and adopt best practices for code quality, maintainability, and documentation.

Apply expertise in AI, ML, LLM, and NLP to build and optimize data processing and feature engineering tasks for various models and applications.

Develop and maintain data pipelines to support large language models (LLMs) and generative AI, ensuring seamless data ingestion, transformation, and model deployment.

Utilizing AWS Bedrock for building and deploying large-scale machine learning models within data migration processes.

Work closely with cross-functional teams to understand business requirements and develop data-driven solutions to address them.

Participate in code and design reviews to ensure the quality, scalability, and reliability of our data infrastructure.

Stay up to date with the latest trends and technologies in the data engineering, AI, ML, and NLP domains, and make recommendations for continuous improvement.

Environment: AWS Redshift SQL, Scala, ETL, OMOP CDM, Databricks, Bedrock, Managed Workflows for Apache Airflow, S3, Redshift, Glue Data Quality, Oracle, DBeaver, GitHub, Agile and Scrum Development Process.

Client: Otsuka, Princeton, NJ Oct 2023 – Jul 2024

Role: Data Engineer III

Responsibilities:

Build and deploy modular data pipeline components such as Apache Airflow DAGs, AWS Glue jobs, and AWS Glue crawlers through a CI/CD process.

Translate business and functional requirements into actionable technical build specifications.

Collaborate with technology teams to extract, transform, and load data from a wide variety of sources.

Work closely with product teams to deliver data products in a collaborative and agile environment.

Perform data analysis and onboarding activities as new data sources are added to the platform.

Utilize data modeling techniques and concepts to support data consumers in designing efficient methods of storage and retrieval.

Evaluate innovative technologies and tools while establishing standard design patterns and best practices for the team.

Implemented Attack Surface Management solutions to continuously monitor, assess, and secure dynamic digital assets, enhancing cyber defense posture and reducing exposure.

Integrated AI-driven behavior analytics with Exabeam for insider threat detection.

Leverage AWS Data processing, analytics, and storage services such as S3, Glue, Athena, and Lake Formation.

Extract and deliver data from various databases including MongoDB, DynamoDB, Snowflake, Redshift, Postgres, and RDS.

Code proficiently with Python, Scala, SQL, YAML, and Spark (PySpark). Expertise in data ETL and validation within OMOP CDM.

Developed and optimized ETL pipelines in AWS Databricks to process large-scale data, improving data processing times by 30%.

Integrated GenAI models for dynamic data enrichment and predictive insights within streaming architectures.

Utilize Apache Airflow as a pipeline orchestration tool.

Optimize Redshift clusters for improved query performance and cost-effective storage solutions during migration.

Employ AWS serverless services such as Fargate, SNS, SQS, and Lambda.

Integrated SCAS, SAST, and DAST/WAS tools into SDLC to enhance code security and compliance.

Streamlined secure DevOps practices within SDLC for robust data protection.

Implemented SCAS, SAST, and DAST/WAS tools to identify vulnerabilities and enforce compliance within secure SDLC frameworks.

Designed and integrated security testing workflows to ensure application security at every stage of the SDLC.

Manage containerized workloads using AWS ECS, ECR, and Fargate for scaling and organization.

Apply data modeling skills to work with analytics teams in designing efficient data structures.

Leverage AWS Bedrock’s AI capabilities to enhance data quality checks and validation during migration workflows.

Automated data validation and deduplication processes within Veeva CRM to maintain a single source of truth for HCP and HCO data.

Ensured data quality and governance in CRM systems to maintain compliance with pharma regulatory standards.

Collaborate with data scientists to optimize data workflows, manage model training datasets, and implement efficient data storage solutions for AI applications.

Operate in agile, scrum, or DevOps environments and teams.

Implement modern software delivery methods such as TDD, BDD, and CI/CD.

Utilize Infrastructure as Code (IaC) practices.

Manage the development lifecycle including development, testing, documentation, and versioning.

Environment: Apache Spark, Scala, Python, SQL, Power BI, GitLab, AWS, Databricks, S3, Bedrock, Glue, Athena, Crawler, Lake Formation, Managed Apache Airflow, CloudWatch, Secrets Manager, Systems Manager, Batch, IAM, OMOP CDM, Glue DataBrew, CloudShell, Snowflake, Boto3, Agile and Scrum Development Process.

Client: Merck, North Wales, PA Jun 2022 – Sep 2023

Role: Sr. Big Data Engineer

Responsibilities:

Proficient in Agile Project Management Methodology and SDLC (Software Development Life Cycle), including requirement gathering, analysis, design, development, and testing of applications using Agile and Scrum methodologies.

Thorough understanding of existing build systems and tools related to product information, releases, and test results.

Expertise in process improvement, data extraction, data cleansing, Scrum data manipulation, and concepts such as normalization and denormalization.

Created ETL data pipelines in an advanced AWS environment (BedRock, EC2, S3, Lambda, Jumpstart, etc.) using AWS Glue.

Skilled in transforming healthcare datasets into the OMOP CDM format for standardized analysis.

Worked with various Amazon Web Services (EC2, ELB, VPC, S3, CloudFront, Elasticsearch, IAM, RDS, Route 53, CloudWatch, BedRock, SNS, Redshift, Kinesis, RDS, Lambda, Glue, Sage Maker, Personalize).

Setup and configuration of AWS Virtual Private Cloud (VPC) components, including subnets, IGW, security groups, EC2 instances, elastic load balancers, and NAT gateways for an Elastic Map Reduce Cluster.

Implemented SCAS, SAST, and DAST/WAS to identify and mitigate vulnerabilities in real-time.

Deployed, managed, and operated scalable, highly available, and fault-tolerant systems on AWS.

Currently managing day-to-day AWS accounts, providing recommendations for supporting global infrastructure, and collaborating with developers and architects in cross-functional areas.

Hands-on experience with AWS CLI interface and designing scalable AWS solutions.

Extensive experience implementing data warehouse solutions in Redshift and migrating data from on-premises databases like Oracle to Redshift, RDS, and S3.

Proficient in AWS SageMaker and successfully developed and deployed a product recommendation system using Matrix Factorization and KNN algorithms.

Conducted extensive research on AWS Personalize and implemented the event ingestion code snippet on our product website.

Engineered end-to-end GenAI solutions to automate data ingestion, transformation, and anomaly detection in real-time.

Assisted in the implementation and maintenance of security and data encryption technologies.

Performed comprehensive analysis of database capacity and performance requirements.

Integrated Veeva CRM data pipelines with enterprise data warehouses to enhance sales and marketing insights.

Experience in building reusable ETL components using Postgres and Snowflake.

Worked extensively on writing Snowflake triggers and automating the ETL process in the Postgres database.

Extracted data from legacy systems into a staging area using ETL jobs and SQL queries.

Performed quality assurance testing and automated error record detection on Postgres.

Collaborated closely with developers in API development using Node.js in the Express framework and developed a service for data obfuscation.

Conducted unit and integration testing on various API components using Mocha and Chai.

Utilized Google Analytics to track visitor flow and interaction on the company website.

Integrated Jupyter Notebooks with the Google Analytics platform for analyzing customer interaction data.

Extensively researched and implemented various regression, classification, and clustering machine learning algorithms in Jupyter Notebooks.

Perform analysis and engineering for high availability and disaster recovery among current and future data center, virtualization, and cloud deployments.

Enhanced threat intelligence pipelines using Recorded Future and ThreatConnect.

Strategically proficient in designing experiments, data collection, analysis, and visualization using various tools and technologies like Tableau.

Environment: Apache Spark, Python, Kafka, Tableau, DataStage ETL, Oracle, OMOP CDM, S3, Node.js, API, Hadoop, AWS, SageMaker, BedRock, Personalize, Python, Maven, Disaster Recovery, GIT, MySQL, PostgreSQL, Sqoop, Flume, JDK 1.8, Agile and Scrum Development Process, Dialog Flow, Chai, Mocha & amp.

Client: Stryker – Cary, IL Jul 2021 – May 2022

Role: Sr. Data Engineer

Responsibilities:

Imported data into HDFS/Hive from multiple relational databases using Sqoop, conducted operations and exported the results back.

Participated in migrating an on-premises Hadoop system to GCP (Google Cloud Platform).

Utilized Spark Streaming extensively to analyze real-time sales data from sources like Kafka at regular intervals.

Optimized Spark joins, conducted troubleshooting and monitoring, and wrote efficient Scala code.

Possessed in-depth knowledge of moving data into GCP using the SQOOP process, using custom hooks for MySQL, and utilizing cloud data fusion to transfer data from Teradata to GCS.

Leveraged big data tools such as Spark (Pyspark, Spark SQL) to perform real-time analysis of insurance transactions.

Executed Spark transformations and actions on large datasets, implemented Spark SQL for complex data manipulations, and worked with structured and semi-structured data stored in a cluster using Data Frames/Datasets.

Transferred previously written cron jobs to airflow/composer in GCP.

Worked with Hadoop, Spark, Kafka, and other big data tools.

Provided support for existing GCP Data Management implementations.

Developed GCP Big Query authorized views for row-level security or data exposure to other teams.

Possessed extensive experience in IT data analytics projects and hands-on experience migrating on-premises ETLs to Google Cloud Platform (GCP) using cloud-native tools such as Big Query, Cloud Data Proc, Google Cloud Storage, and Composer.

Developed Spark code using Scala and Spark-SQL to enhance data processing speed.

Created Oozie workflow engines to execute multiple Spark jobs.

Utilized Spark for optimizing and improving the performance of existing algorithms in Hadoop using Spark SQL, Data Frame, pair RDDs, and Spark YARN.

Denormalized data during the transformation from Netezza and loaded it into NoSQL databases and MySQL.

Loaded data into Big Query tables from an internal stage using SnowSQL.

Performed import and export operations between the internal (Big Query) and external stages (GCS).

Possessed a strong understanding of partitions and bucketing concepts in Hive, designing both Managed and External tables to optimize performance.

Developed internal dashboards for the team using Power BI tools to track daily tasks.

Environment: GCP, Spark, Kafka, SnowSQL, GCS, DataStage ETL, Data Fusion, My SQL, YARN, Oozie, Scala, Big Query, Data proc, RDD’s, NoSQL, Hive & Power BI.

Client: First Republic Bank, San Francisco, CA Sep 2019 – Jun 2021

Role: Data Engineer

Responsibilities:

Developed data pipelines using Google Cloud Platform (GCP) services such as IoT Registry, Pub/Sub, Dataflow, BigQuery, Data Prep, Data Studio, and AI Platform.

Created and deployed applications on GCP using Data Proc, Dataflow, Composer, BigQuery, Bigtable, Cloud Storage, GCS, and various DAG operators.

Migrated existing data pipelines from Hive to GCP.

Designed and implemented data ingestion, transformation, and curation functions on GCP using GCP native services and Python.

Optimized data pipelines for performance and cost efficiency in large-scale data lakes.

Designed and automated BigQuery tables and Google Cloud Functions to enable reporting, analysis, and modeling.

Developed custom UDFs in BigQuery using Node.js and incorporated them into the data pipeline.

Utilized Python for scripting purposes, leveraging a wide range of technologies.

Applied extensive experience in GC networking, web, and mobile services to enhance team and client efficiency.

Worked on developing and supporting databases and related ETL processes for both batch and real-time processing.

Strong understanding of issue triaging and resolution protocols in big data systems.

Designed, tested, and implemented data migration, ingestion, processing, and quality frameworks capable of handling large volumes of data using Airflow, PySpark, Python, and BigQuery.

Conducted design and code reviews to ensure high-quality deliverables.

Actively engaged with data customers to gather feedback on developed data solutions and created documentation for best data engineering practices.

Developed Hive scripts, Hive UDFs, and Python scripts, and utilized Spark (Spark-SQL, Spark Shell) for data processing in Cloudera and Hortonworks.

Collaborated closely with teams and clients in accessing and managing cloud services and resources provided by Microsoft.

Built a system for analyzing column names across on-premises databases and identifying personal information columns during data migration to GCP.

Designed and developed Scala code for data extraction from cloud-based systems and applied transformations.

Used Sqoop to import and export data between HDFS and MySQL databases.

Implemented optimized joins for data analysis using MapReduce programs.

Created continuous integration and continuous deployment (CI/CD) pipelines on AWS to automate steps in the software delivery process.

GC Storage Account played a crucial role in data storage within Microsoft as a key component.

Processed and transformed large datasets of structured, unstructured, and semi-structured data in Cloudera and Hortonworks.

Responsible for implementing, monitoring, maintaining, and managing Microsoft services.

Implemented data partitioning, dynamic partitions, and buckets in HIVE and Impala to enhance data access efficiency.

Utilized Rallying Tool in an Agile environment for effective management and tracking of user stories and tasks.

Extensively worked with Hive SQL, join operations, writing custom UDFs, and optimizing Hive queries.

Designed and developed job flows using Apache Airflow.

Conducted data discrepancy analysis and recommended solutions based on root causes.

Environment: HDFS, Python Scripting, ETL, Map Reduce, Hive, Impala, Spark-SQL, Spark Streaming, Sqoop, Pyspark, Spark Shell, Hadoop, Java, GCP, Big Query, JDBC, Python, Scala, UNIX Shell Scripting, Git, CI/CD pipelines (Jenkins) & Apache Airflow.

Client: Centene Corporation, Saint Louis, MO Feb 2018 – Aug 2019

Role: Data Engineer

Responsibilities:

Developed PySpark pipelines to transform raw data from multiple formats into Parquet files for downstream system consumption.

Utilized AWS Glue services, including crawlers and ETL jobs, to catalog Parquet files and perform data transformations based on business requirements.

Collaborated with various AWS services such as S3, Glue, EMR, SNS, SQS, SageMaker, BedRock, Lambda, EC2, RDS, and Athena to process data for downstream customers.

Created libraries and SDKs to facilitate JDBC connections to Hive databases and query data using Play Framework and various AWS services.

Implemented Spark scripts to accelerate data loading from Hive to Amazon RDS (Aurora).

Developed Hive views for application usage through Spark SQL.

Implemented data security measures using Apache Ranger, including row-level filters and group-level policies.

Leveraged MDM tools to create a single source of truth for product, supplier, and patient data, driving efficiency in supply chain and patient services.

Normalized data to meet business needs, involving data cleansing, datatype modifications, and various transformations using Spark, Scala, and AWS EMR.

Used Terraform scripts to automate step execution in EMR for loading data into ScyllaDB.

Created CI/CD pipelines using tools like Jenkins and Rundeck to schedule daily jobs.

Developed Sqoop jobs to import data from Oracle to AWS S3.

Built a utility to transform and export data from AWS S3 to AWS Glue and send alerts and notifications to downstream systems (AI and data analytics) upon data readiness.

Automated Hadoop cluster scaling, server provisioning, and feature deployment through Groovy-scripted Jenkins CI/CD pipelines and Ansible playbooks.

Utilized Jenkins CI/CD to retrieve code from version control systems like GitHub and build projects using Apache Maven and Gradle, storing resulting artifacts in repositories like Nexus.

Converted Hive/SQL queries into Spark transformations using Spark RDDs, Python, and Scala.

Developed pipelines to audit application metrics using AWS Lambda and Kinesis Firehoses.

Created an end-to-end pipeline to export data from Parquet files in S3 to Amazon RDS.

Optimized Hive query performance using Hive LLAP and various other techniques.

Environment: AWS, ETL, Spark, Pyspark, Python, Hadoop, Hive, Sqoop, Play framework, Apache Ranger, Maven & Gradle, Terraform, Nexus, JDBC, ScyllaDB, Hive, EMR, Zeppelin, Jenkins CI/CD, Ansible Playbooks, Rundeck and Scala.

Client: ICICI Bank, Hyderabad, India Nov 2015 – Jan 2018

Role: Data Analyst / BI Developer

Responsibilities:

Participated in testing procedures and data using PL/SQL to ensure data integrity and quality in the data warehouse.

Worked to maintain high data consistency levels between diverse source systems, including flat files, XML, and SQL databases.

Developed and executed ad-hoc data queries across multiple database types to identify the system of records, data inconsistencies, and data quality issues.

Created complex SQL statements to extract and package/encrypt data for customer delivery.

Provided business intelligence analysis to decision-makers using an interactive OLAP tool.

Created T/SQL statements (Select, Insert, Update, Delete) and stored procedures.

Contributed to defining source-to-target data mappings, business rules, and definitions.

Ensured compliance of data extracts with the initiatives of the Data Quality Center.

Generated metrics reports, conducted data mining, and analyzed trends in the helpdesk environment using Access.

Utilized SQL Server Integration Services (SSIS) to integrate and analyze data from multiple heterogeneous information sources.

Constructed reports and report models using SSRS to facilitate end-user report building.

Developed Excel charts and pivot tables for ad-hoc data analysis.

Created column store indexes on dimension and fact tables in the OLTP database to improve read operations.

Worked as a data visualization consultant supporting the Risk Consulting Team in model documentation and data visualizations for the Data Science Team, which develops credit and market risk predictive models for banking clients.

Served as a Tableau Desktop Developer focusing on creating high-end visualizations driven by data from various sources, including flat files, SQL Server, and MS Excel.

Developed stories and dashboards using multiple data sources, blending them into a single worksheet in Tableau Desktop.

Extensively involved in dashboard development, including creating Tableau extracts, connectors (live and extract), formatting, and report operations (sorting, filtering, ranking, top-N analysis).

Utilized advanced analysis actions, calculations, parameters, background images, maps, trend lines, statistics, log axes: groups, and hierarchies in Tableau.

Designed, deployed, integrated, and maintained Master Data Management (MDM) systems.

Utilized advanced data visualization and representation techniques in Tableau to provide an easy-to-understand interface for end-users to identify critical areas within their data.

Conducted training sessions for end-users worldwide to use Tableau effectively.

Utilized corporate templates to create and improve client communications, such as company reports, screen presentations in PowerPoint, and various reports in MS Excel/Tableau, following specified standards.

Involved in designing and producing visual communication materials, including charts and financial presentations, utilizing PowerPoint, Think cell, and Tableau specifically for the Confidential Leadership Team.

Environment: Tableau, SQL, MS SQL Server, PL/SQL, Flat Files, T/SQL, XML, OLAP, SSIS, SSRS, ER win & MS Suite.

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