Hands on modeling experience is required, Experience with Big Data technologies such as Hadoop/Hive, Ability to do financial and statistical data analysis and modelling, Knowledge of Machine learning is desirable, 6-7 years experience Data Engineeirng / Business Intelligence, Good working knowledge of RDBMS and Datawarehouse environment, Familiarity with AWS technologies preferred, Familiarity with Machine Learning tools and technologies, Analyzing data sources for availability and quality, Identifying areas of opportunity to improve or enhance existing data processes, Work closely with Research Modelers, external and internal data suppliers throughout The Hartford in analyzing data sources, being subject matter data expert, defining business requirements, providing data, and assisting in high level data analysis, Coordinating with Enterprise Data Warehouse, internal/external data suppliers and service operations, Experience in creating and tuning SQL Queries, Experience with Indexes and basic Data Base Design is a plus, Self-starter with a willingness to become a data expert and to learn new skills, Results oriented with the ability to multi-task and adjust priorities when necessary, Star and Snowflake schema data analysis, modeling and architecture, ETL and ELT architecture and development using tools such as Talend and Pentaho. Provides technical expertise in systems, technical infrastructure, tools, modeling, external interfaces, and other technical areas. MSSQL, MySQL, PostgreSQL), Auto software deployment technologies (e.g. Demonstrate pleasant and positive interactions with others to meet customer expectations, and provide follow up with customers. Possessing strong technical skills rooted in substantial training as an engineer. Naturally it’s going to be filled with the work experience, successful projects along with the firms or companies he/she worked for. Bachelor's degree in Computer Science with Mathematics and Statistics or equivalent subject; Masters preferred. Write and present information in a clear and concise way. 7. Remains calm and is able to manage conflict, and works well with a diverse workforce, Activities include Operations Research development, ad hoc and project support, line of business product segment support, operational leadership, production deployment, maintenance, quality measures and documentation, Perform data asset adoption activities, with internal and external customers, including presentations to all levels of management, training, use case development, and consulting activities, Act as a liaison and SME between business and IT resources, Perform cost benefit analysis of concepts and solutions to drive prioritization, Ability to create and follow a strategic plan and report progress to management, Ability to articulate and train technical concepts to a non-technical audience, Drive deep understanding of vendor segmentation, behavior and satisfaction; as well as Amazon operational performance by identifying, developing, and executing analyses, Champion data and metrics instrumentation during the development lifecycle for new products and services, Support the business team with ad hoc data requests and analytical needs, Experience in designing and operating large Data Warehouses, Strong data modelling/architecture skills, Experience with enterprise class BI and Analytics tools such as Tableau, R/SAS, and OBIEE, Experience in developing Data Engineers, Business Intelligence Engineers and Business Analysts, Design and scripting experience in one of Python, Perl, Shell script, Master's degree in a quantitative or technical field, Work together with the Solution- and Data Architect to design the next generation big data platform to support the digital marketing platform, Design and implement a new big data solution which supports high amounts- and velocity of data, supports future growth, use latest big data technologies/techniques, is cloud supported and in-line with Shell’s strategies, Design and implement modern data pipelines to extract, clean and process data in batch and real-time from different data sources, Use the latest development, testing and deployment techniques to quickly deploy new releases to e.g. unstructured logs, XML, JSON, flat files), Hands-on experience with Big Data ecosystem tools such as Hive, Pig, Sqoop and Spark; and experience with NoSQL databases (Hbase as an example) would be an asset, Programming skills in R or Python would be an asset, Experience with UNIX tools and shell scripting, Solid SQL skills for querying relational databases (e.g., SQL Server, DB2, MySQL, Sybase), Experience using and implementing visualization tools like D3, Tableau or Qlikview, Ability with collaboration tools such as Confluence is an asset. Maintain productivity and uses knowledge strategies to increase knowledge base. Cassandra, HBASE, MongoDB), SQL-based technologies (e.g. Learn and understand a broad range of Amazon’s data resources and know how, when, and which to use, Collaborating with engineers to implement the design of the data architecture, Manage and execute entire projects or components of large projects from start to finish including project management, data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations, 5+ years of professional experience in business analytics at scale, Experience solving complex quantitative business challenges; experience in the development of predictive analysis is a strong plus, Knowledge of economics modeling, machine learning and predictive analytics, Professional traits that are not unique to this position, but necessary for Amazon leaders: Exhibits business judgment; Has relentlessly high standards; Dives deep; Thinks strategically, but stays on top of tactical execution; Thinks big and has convictions; Results oriented, Designing and building an AI and data platform to help us deploy data- and machine learning-driven solutions across different businesses, Testing new technologies and architectures to us find the best ways to work with our unique data sets, Leading a nascent data engineering team to work with product managers, data scientists, and developers to enable modern data solutions, 3+ years implementing big data solutions in industry on a cloud platform, preferably AWS, Proven ability to develop pipelines that can serve machine learning models that solve business problems, Strong understanding of modern data architectures and big data solution architectures, Proficient in SQL, *nix CLI tools (grep/sed/awk/BASH, etc), and Python, Familiarity with Java and an understanding of the JVM ecosystem, Familiarity with tools like Docker, Kafka, Cassandra, Spark, etc, Play a leading role in designing, developing and implementing a Graph database that contains multiple data sets from both internal and external sources, Lead the setup of data pipelines of new internal and external data sets into the database, Work with data scientists to help dedupe and fuzzy match data, Work with software engineers on developing APIs, 5+ Years’ Experience in data engineering, including, Design, develop, and support database enhancements and data loading (includes some night and weekend support), Design and develop new database technologies and integration solutions, Ensure successful and timely completion of assigned tasks, Support our Customer Operations team to resolve customer issue related to data, Support our Legal team by providing reports to assist in subpoena requests, Procure, test and recycle telephone numbers and maintain number inventory systems, Work with Data Architects and Product Managers to define and create reports that meet business requirements, quality, and performance, Responsible for the design, development, testing and implementation of BI reports, dashboards, scorecards and OLAP Cubes, Responsible for data integration with multiple databases, Collaborate with business users to develop, build, and implement reporting capabilities for LogMeIn Partners, Agents, and Consumers, Support ETL development activities when needed, Work with users to understand and document business requirements, Designing and implementing the big data platform/solutions used to ingest and process Amazon-scale traffic data, Collaborating with partner data engineers to ensure data consistency across teams and the broad adoption of best-practices, Experience building data warehousing and analytics projects using AWS technologies such as Redshift, S3, EC2, EMR and data pipelines, Experience with big data technologies (Hadoop, Hive, Hbase, Pig, Spark, etc. Senior Validation Engineer Resume Summary : Senior Validation Engineer with strong knowledge of Documentum, Service Now, Trackwise Change Management and Agile PLM. 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