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 Hands-on modeling, design, configuration, installation, performance tuning, and sandbox POC.
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 Experience in Data Vault Modeling is a must.
o Data Modeling certification is a plus.
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 Data Modeler) 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 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.
o Strong experience with popular database programming languages including [SQL, PL/SQL, others] for relational databases and certifications on upcoming [NoSQL/Hadoop oriented databases like MongoDB, Cassandra, others] for nonrelational databases.
o Strong experience working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures, and integrated datasets using traditional data integration technologies. These should include [ETL/ELT, data replication/CDC, message-oriented data movement, API design, and access] and upcoming data ingestion and integration technologies such as [stream data integration, CEP, and data virtualization].
o Strong experience working with and optimizing existing ETL processes and data integration and data preparation flow and helping to move them in production.
o Basic experience working with popular data discovery, analytics, and BI software tools like Tableau for semantic-layer-based data discovery.
o Demonstrated success in working with large, heterogeneous datasets to extract business value using popular data preparation tools.
o Adept in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse, and automation of data flows between data managers and consumers across an organization.
o Previous experience as a data engineer or in a similar role
o Technical expertise with data models, data mining, and segmentation techniques
o Knowledge of programming languages (e.g. Java and Python)
o Hands-on experience with SQL database design o Great numerical and analytical skills
o ETL and Data Lakehouse
o Knowledge of Informatica
o Knowledge of Cloudera
o Operating system knowledge for Unix, Linux, Windows, and Solaris
o Hadoop
o Apache Spark
o Data mining and modeling
o Degree in Computer Science, IT, or similar field; a Master’s is a plus.
o Data engineering certification is a plus.
