Below are the 6 Cloud Data Engineers with multiple systems expertise.  The Thor Group® provides cloud data engineers with your systems & industry experience on a consulting, contracting or direct hire basis.

Would hiring a cloud data engineer/data scientist with experience working in consulting and IT services industries help meet the needs of your organization? Educational attainment is composed of Bachelor of Professional Studies with Business Concentration and Master of Business Administration with Finance Concentration. Cloud expertise consists of AWS, Google Cloud Platform, IBM Cloud, Azure Cloud, Hadoop, DynamoDB and Spark. Computer skills include Python (Anaconda, scipy, numpy, scikit-learn, TensorFlow), SQL, Visual Basic, C#, C++, Java, R, MATLAB, Mathematica, SPSS, SAS, Oracle, SQL Server, MySQL, noSQL, XML and JSON. Also, adept in machine learning: clustering, association, regression, neural networks, anomaly detection, both (un)supervised learning.

Acting as a cloud data engineer/data scientist in a consulting company, led requirements gathering and analysis in Agile environment for team members, and other stakeholders. Used Python-based machine learning such as pandas, numpy, scikit-learn, boto (accessing Amazon AWS), and TensorFlow. In addition, performed Bayesian time series and econometric analysis of exogenous market variables, modeled in open source software. Tools/languages used were R, Python, Spark, AWS, Azure, Excel, SQL Server, SSIS, SSRS, and VBA.

While working for the same consulting company as a data scientist, served as business and technical advisor with hands-on experience in predictive analytics and reporting in cloud-based systems, responsible for assessing business scenarios, implementing risk management policies, and creating quantitative models. This professional conducted business analysis and quantitative modeling of large-scale client/server application and databases saving clients up to $5 million in infrastructure costs. Furthermore, coded global large scale data analysis application with cloud-based tools in Amazon AWS using MapReduce (“divide and conquer”) software to produce deliverables in brief timeframes. Used Microsoft Azure Machine Learning Studio and Virtual Machines to produce predictive marketing models.

Could the future success of your organization be helped by this cloud data engineer with work experience in retail, financial services and packaging industries? Educational attainment is composed of Bachelors in Computer Science/Business Admin. Has experience with AWS Cloud IAM, Data pipeline, EMR, S3, EC2, AWS CLI, SNS, and other services. Cloud platforms and tools include S3, AWS, EC2, EMR, Lambda services, Microsoft Azure, Adobe Cloud, Amazon Redshift, Rackspace Cloud, Intel Nervana Cloud, Open Stack, Google Computer Cloud, IBM Bluemix Cloud, MapR cloud, Elastic Cloud and Anaconda Cloud. Knowledge in Big Data Platforms consists of Hadoop, Cloudera Hadoop and Hortonworks. Hadoop Ecosystem Components include Sqoop, Kibana, Tableau, AWS, HDFS, Hortonworks and Apache Airflow.

Contributed to the organization’s success by creating modules for Apache Airflow to call different services in the cloud including EMR, S3, and Redshift. Acting as a cloud data engineer in a retail company, created AWS Lambda function for extracting the data from Kinesis Firehose and post the data to AWS S3 bucket on scheduled basis (every 4 hours) using AWS Cloud Watch event. Some of the tasks were creating and maintaining the data warehouse in AWS Redshift, as well as implementing Spark in EMR for processing Big Data across Data Lake in AWS System.

Supported organizational goals and objectives by developing multiple Spark Streaming and batch Spark jobs using Scala and Python on AWS. While working as a Big Data engineer in a financial services company, executed Hadoop/Spark jobs on AWS EMR using programs, data stored in S3 Buckets. Other duties were ingesting data through AWS Kinesis Data Stream and Firehose from various sources to S3, as well as working with Amazon Web Services (AWS) and involved in ETL, Data Integration, and Migration. Furthermore, documented the requirements including the available code which should be implemented using Spark, Amazon DynamoDB, Redshift, and Elastic Search.

Is impacting your organization with a sr. Google Cloud data engineer who worked in education and financial services industries a current business consideration? Educational attainment is composed of Master’s Degree in Healthcare Administration. Professional certifications include Google Cloud Professional Data Engineer Certification, Modernizing Data Lakes and Data Warehouses with Google Cloud Platform, and Building Resilient Streaming Analytics Systems on GCP Certification. Technical skills include Linux, GitHub, Gitlab, Git, VS Code, JavaScript, ReactJS, Django, Python, SQL, Kubernetes, Docker, Chatbots, Basic SQL, Intermediate websites with HTML and CSS, Java/J2EE, and Maven/Git/Jenkins.

While employed in a learning center as a sr. Google Cloud data engineer, worked in IT data analytics projects, using hands on experience in migrating on premise ETLs to Google Cloud Platform (GCP) using cloud native tools such as BIG query, Cloud Data Proc, Google Cloud Storage, and Composer. Also, utilized expert knowledge of public cloud providers (Google Compute Platform, OpenStack) and their technology offering, APIs and enterprise integration points. Moreover, worked on GCP Dataproc, GCS, Cloud functions and BigQuery.

Part of the job as a Google Cloud data engineer in a financial services company was to migrate nine micro services to Google Cloud Platform from skava and have one more big release planned with 4 more micro services. Moreover, performed the migration of mobile application from skava to cloud (Google Cloud) by making the chunk of code to micro services. Furthermore, set up Alerting and monitoring using Stackdriver in GCP.

What contributions could an Azure cloud database engineer with experience working in IT services and consulting industries bring to your organization? Educational attainment is composed of Bachelor of Computer Application. Professional certifications involve Microsoft Certified: Azure Solutions Architect Expert, Microsoft Certified: Azure Database Administrator and MCSA: Cloud Platform Certified. Technology skills include Microsoft Azure IaaS, SaaS & AWS RDS & EC2), Azure data factory, AZURE data lake, MSSQL, Azure SQL, AWS, Java, ASP .NET, CRM, .Net, Java, C, PowerShell, Python, ARM, PqSql, MangoDB and MySQL.

Challenges like providing specialized knowledge in cloud database, database technologies & business intelligence solutions to some of the tasks were welcomed and successfully completed on a regular basis. As an Azure cloud database engineer/consultant in an IT services company, key responsibilities were preparing RFI, RFP, & BM on Database Technology (MS-SQL), Middleware (SharePoint Infrastructure & Migration), Cloud Infrastructure (Amazon Web Services and Microsoft Azure). Part of the job was to evaluate IT setup, plan, design and migration of application HA & DR -Webs Apps, Database & Middleware to Amazon Web Services and Microsoft Azure Cloud from on-prim environment. Moreover, worked on the installation, configuration, administration, automation, performance tuning and migration Databased include MSSQL Server from On-Prim to Cloud (AWS-RDS & EC2 & Azure-IaaS & PaaS) or vice versa.

Shared expertise by working on Azure SQL Database High Availability architecture, DR models, and multi-site deployment. Serving as an Azure cloud database consultant for the same IT services company, worked on Azure’s advanced threat protection to assist with data classification, vulnerability assessment, and threat detection. Also, used experience on Azure database data store for Performance, analytics, tuning, metrics, logging Publish data using Power-BI. Furthermore, worked on database DevOps using Azure Team foundation for setup, deploy, build, release pipeline from lower to higher environment.

Would it be beneficial for your organization to employ a senior Oracle Cloud data architect with work experience in logistics and telecommunications industries. Educational attainment is composed of Bachelors in Mechanical Engineering and Masters in Computer Science. Professional certification involves Oracle Certified Professional (DBA). Technical knowledge consists of Azure SQL DB, Azure Data Studio, GCP, AWS, SQL Server, Azure SQL DB, MySQL, PostgreSQL and Azure Cosmos DB. Key accomplishment was directing 24×7 monitoring of various Oracle, Cassandra, MySQL, MongoDB Databases on Linux, AWS, Azure & Google Cloud Platforms. Additional skills involve Cloud: AWS S3, Dynamo DB, Oracle Cloud Infrastructure, Informatica (OCI), Shell scripting, SQL, PL/SQL, ABAP/4, C, Java, Perl, Python, JSON, R, Scala, SQL Java, EJB, Power Shell and Yaml.

Serving in a logistics company as a senior Oracle Cloud data architect, worked on Data Classification and Encryption / Masking, Oracle Cloud Infrastructure (OCI), and Oracle Data Integrator (ODI). Part of the job was to work on Operational Data Store (ODS) Data Modeling of Autonomous Data Warehouse on Oracle Cloud Platform. Some of the tasks were reviewing LOE (Level of Effort) and Cost Estimates required for data center migration to Azure Cloud Environment, as well as reviewing Cloud Solutions using various AWS Services including EC2, S3, Glacier, EFS, DynamoDB, Redshift etc.

As a sr. DBA (Application Development Associate Manager) in a telecommunications company, worked as a Senior Database Developer and Data Warehouse Specialist, responsible for Credit Apps Decision Support System (DSS), and 24×7 monitoring of Oracle, PostGreSQL, Cassandra, MySQL, MariaDB Production Databases on Linux, AWS & Azure Cloud platforms Familiar with Google Cloud Platform (GCP) services like cloud SQL, compute engine, cloud load balancing, cloud storage, stack driver monitoring and cloud deployment manager. Furthermore, reported on Project Status to Client & all Stakeholders through Dashboards/ MIS Reports/ Project Walkthroughs.

Would a detail-oriented cloud data engineer/data scientist who worked in mental health, business solutions and financial services industries fit in your organization? Educational background is composed of Bachelor of Electronics and communication and MS (Computer and Information Sciences specializing in Data science). Skills in cloud computing include Amazon web services like S3, EC2, Redshift and Lambda Function. Additional technological knowledge consist of Python, R, SAS, Tableau, C++, JavaScript, ReactJS, MySQL Workbench, MongoDB, SQL, Power Query and PostgreSQL.

Some accomplishments included being responsible for gathering Requirements, analysis, Design, development, testing and deployment. Serving in a mental health facility as a cloud data engineer/data scientist, migrated databases from prem to AWS, Azure, and Google Cloud. Furthermore, migrated data in to cloud services like SQL DB and Azure Blob.

Shared expertise by improving the reporting dashboards and the functionality of planning tools. Built a statistical analysis model on large datasets and reduced data processing time by 95%. While employed in a business solutions company as a data analyst, utilized AWS services with focus on big data analytics, enterprise data warehouse and business intelligence solutions to ensure optimal architecture, scalability and flexibility.