o Performing statistical tests on large datasets to determine data quality and integrity (Data Profiling).
o Evaluating system performance and design, as well as its effect on data quality.
o Collaborating with database developers to improve data collection and storage processes.
o Running data queries to identify coding issues and data exceptions, as well as cleaning data.
o Gathering data from primary or secondary data sources to identify and interpret trends.
o Reporting data analysis findings to management to inform business decisions and prioritize information system needs.
o Documenting processes and maintaining data records.
o Adhering to best practices in data analysis and collection.
o Keeping abreast of developments and trends in data quality analysis.
o Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning).
o Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models
o Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.
o Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.
o Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.
o Analyze and organize raw data
o Evaluate business needs and objectives
o Interpret trends and patterns
o Conduct complex data analysis and report on results
o Explore ways to enhance data quality and reliability
o Identify opportunities for data acquisition
o Collaborate with data scientists and architects on several projects
o Using Extract Transform Load operations (the ETL / ELT process)
o Researching new methods of obtaining valuable data and improving its quality
o Creating structured data solutions using various programming languages and tools
o Mining data from multiple areas to construct efficient business models
o Collaborating with data analysts, data scientists, and other teams.
o Bachelor’s degree in computer science, information technology, engineering, or equivalent (KSU is a plus)
o Degree in Computer Science, IT, or similar field; a Master’s is a plus.
o Data Quality certification is a plus.
o Experience with SQL, ODI.
o Experience with Alteryx is a plus.
o Experience in data analysis.
o Proficiency in programming languages, including Structured Query Language (SQL) and JavaScript.
o In-depth knowledge of statistical methods and tests.
o Extensive experience with statistical packages, such as MS Excel.
o Exceptional analytical skills.
o Advanced problem-solving skills.
o Knowledge of best practices in data analysis.
o Excellent interpersonal and communication skills.
o 1+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, NoSQL data platform technologies, and ETL and data ingestion protocols).
o Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-center contexts required.
o Good knowledge of metadata management, data modeling, and related tools (Erwin or ER Studio or others) required.
o Experience in team management, communication, and presentation
o experience with SQL, ODI, and ETL/ELT.
o Experience building or maintaining ETL processes
o Communication skills, especially explaining technical concepts to non-technical business leaders.
o Technical Knowledge/Skills
o Strong experience with advanced analytics tools for Object-oriented/object function scripting using languages such as [R, Python, Java, KNIME, others].
o Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata, and workload management. The ability to work with both IT and business in integrating analytics and data science output into business processes and workflows.
