o Analyze and organize raw data.
o Build data systems and pipelines.
o Evaluate business needs and objectives.
o Interpret trends and patterns.
o Conduct complex data analysis and report on results.
o Prepare data for prescriptive and predictive modeling.
o Combine raw information from different sources.
o Explore ways to enhance data quality and reliability.
o Identify opportunities for data acquisition.
o Develop analytical tools and programs.
o Collaborate with data scientists and architects on several projects.
o Creating systems for collecting data and for processing that data.
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 engineering certification is a plus.
o Experience with SQL, ODI.
o Experience with Alteryx is a plus.
o Experience building or maintaining ETL processes.
o Communication skills, especially explaining technical concepts to non-technical business leaders.
o Experience with advanced analytics tools for Object-oriented/object function scripting using languages such as [R, Python, Java, KNIME, others] is a plus.
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 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 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 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 Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-center contexts required.
o Knowledge of Informatica tools (MDM – Data Integration).
o Knowledge of Cloudera.
o Operating system knowledge for Unix, Linux, Windows, and Solaris
o Knowledge of Hadoop & Apache Spark
o Knowledge of Data mining and modeling
