Role: Solutions Engineer- Azure Snowflake Experience: 8+ -15 yearsLocation: Chicago, IL /Cincinnati, OH Onsite Type of Hire FTE Direct hire Responsibilities and Qualifications: Must have total 8+ yrs. in IT and 3+ years' in Data warehouse, ETL, BI projects and reporting. Must have experience at least two end to end implementation of Snowflake cloud data warehouse and 3 end to end data warehouse implementations on-premise preferably on Oracle. Must have experience working on MicroStrategy, Tableau and other reporting tools. Expertise in Snowflake data modelling, ELT using Snowflake SQL, implementing complex stored Procedures and standard DWH and ETL concepts Expertise in Snowflake advanced concepts like setting up resource monitors, RBAC controls, virtual warehouse sizing, query performance tuning, Zero copy clone, time travel and understand how to use these features Expertise in deploying Snowflake features such as data sharing, events and lake-house patterns. Hands-on experience with Snowflake utilities, SnowSQL, SnowPipe, Big Data model techniques using Python. Experience in Data Migration from RDBMS to Snowflake cloud data warehouse Deep understanding of relational as well as NoSQL data stores, methods, and approaches (star and snowflake, dimensional modelling) Experience with data security and data access controls and design Experience with Azure data storage and management technologies. Build processes supporting data transformation, data structures, metadata, dependency and workload management. Proficiency in RDBMS, complex SQL, PL/SQL, Unix Shell Scripting, performance tuning and troubleshoot. Provide resolution to an extensive range of complicated data pipeline related problems, proactively and as issues surface. Must have expertise in Azure Platform. Certified Snowflake cloud data warehouse Architect (Desirable Should be able to troubleshoot problems across infrastructure, platform and application domains. Must have experience of Agile development methodologies. Strong written communication skills. Is effective and persuasive in both written and oral communication. Primary Roles and Responsibilities: Data Source Identification: Supports the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for purpose. Performs initial data quality checks on extracted data. Problem Formulation: Translates business problems within one's discipline to data related or mathematical solutions. Identifies what methods (for example, analytics, big data analytics, automation) would provide a solution for the problem using tools and technologies like cloud technology (Snowflake, Azure), Big Data Technologies (Oracle, MySQL, Spark, Hive) etc. Shares use cases and gives examples to demonstrate how the method would solve the business problem. Data Strategy: Understands, articulates, interprets, and applies the principles of the defined strategy to unique, moderately complex business problems that may span one or main functions or domains. Data Quality Management: Promotes and educates others on data quality awareness. Profiles, analyzes, and assesses data quality. Tests and validates data quality requirements. Continuously measures and monitors data quality. Delivers against data quality service level agreements. Manages operational Data Quality Management procedures. Manages data quality issues and leads data cleansing activities to remove data quality defects, improve data quality, and eliminate unused data. Data Visualization: Generates appropriate graphical representations of data and model outcomes. Understands customer requirements to design appropriate data representation for multiple data sets. Works with User Experience designers and User Interface engineers as required to build front end applications. Applied Business Acumen: Provides recommendations to business stakeholders to solve complex business issues. Develops business cases for projects with a projected return on investment or cost savings. Translates business requirements into projects, activities, and tasks and aligns to overall business strategy. Exploratory Data Analysis: Collects and tabulates data and evaluates results to determine accuracy, validity, and applicability. Supports the identification and application of statistical techniques based on requirements. Applies suitable technique under direction from leadership. Assists in the planning, design and implementation of exploratory data analysis research projects. Understands existing statistical models and identifies and recommends statistical models based on hypothesis. Monitor performance of individual contributors and help manage their professional development Provides thought leadership and coaches within data engineering practice Managed overall risk, mitigating risks/issues with client and leadership and Involved with leadership on resource planning