The position of Sr. Business Intelligence Data Analyst/ Engineer must possess a solid knowledge of the principles and practices of enterprise data warehouse development, data modeling and testing and story-telling through data analytics. Job Duties and Responsibilities:
Serve as an internal data consultant, participating in data integration discussions.
Participates in the design, development, validation and testing of new or revised reports. Works with user to verify results and content, develops error or exception reports when applicable and receives user sign off on completed work.
Analyze and evaluate highly complex business and market data; interpret data for the purpose of determining organizational/program performance, trends and/or probability.
Design and apply forecasting and predictive modeling techniques to enhance strategic thinking and business planning.
Efficiently and effectively operate across multiple projects simultaneously and assume responsibility for the appropriate data/information architecture, design and quality.
Meet with key stakeholders to present and review data output to improve operational performance, support decisions, and enhance planning efforts.
Mentor and train on the appropriate usage of data marts, enterprise data warehouse and other data sources used in reporting and analytics, including reporting and visual use cases.
Uses and promotes established Software Development Life Cycle (SDLC) standards, QA and change control procedures.
As a member of the Data Warehouse Team, you will be involved in all aspects of:
Designing, implementing, maintaining, and supporting end-to-end ETL solutions, as well as data warehouse and cubes.
Developing, refining and maintaining the security, quality and integrity of data in ETL solutions and the data warehouse at large.
Establishing, implementing and upholding data integration standards and methodologies.
Creating and executing test plans for ETL and data integration solutions
Monitoring ETL jobs to verify execution, maintain performance and resolve data integration issues as they arise.
Implementing, maintaining and supporting the data quality, data catalog and master data management initiatives of data marts and the enterprise data warehouse system.
Working with Database and System Administrators to establish and enforce best practices for availability, performance, and data security.
Privatively designing support activities around data integration; such as on-going data validation and performance tuning.
Participating in code and design review to ensure alignment to standards and best practices.
Reviewing existing data structures and recommend optimizations and redesigns, as warranted.
Serves as a technology advocate throughout the IT organization to help promote the effective use of the data/information architecture to meet business needs and to build sustained competitive advantage for the enterprise.
What You'll Need
Bachelor’s Degree in Computer Science, Information Systems, or other related field, or 7+ years equivalent work experience required.
Minimum of 7 years’ experience designing, developing and tuning complex, large (TB) database management systems in support of operational reporting, decision support, complex data analysis and system integration.
Minimum of 7 years’ experience working with and tuning Microsoft SQL Server or other similar relational database management systems.
Minimum of 7 years’ experience in data modeling, database design (multi-dimensional and data warehouse), data integration and ETL.
Master’s Degree is preferred.
Supply chain industry experience preferred.
Knowledge of and experience with cloud data solution offerings (Azure Data Lake, Data Factory, Data Management Gateway, Azure Storage Options, DocumentDB, Data Lake Analytics, Stream Analytics, EventHubs, Azure SQL, etc.)
Experience with big data tools: Hadoop, MapReduce, HBase, oozie, Flume and Pig.
Experience and knowledge of cost-optimized cloud deployments spanning compute, network and storage.
Experience of message queuing, stream processing and highly scalable big data stores.
Experience with NoSQL databases, such Cassandra, MongoDB, CosmosDB.
Experience working with DevOps tools: ADO, Git, Jenkins, Dockers, etc.
Experience with stream-processing systems: Storm, Spark-Streaming, etc.
Experience in creating advanced statistics such as: regression, clustering, decision trees, exploratory data analysis methodology, simulation, scenario analysis and modeling.
Experience/ keen interest in exploring latest technologies and programming languages.
Have strong interest in future path of to design, develop and support Machine Learning technologies, algorithms and models in support of business initiatives including:
Determine the appropriate algorithms to solve a given problem through testing, analysis, and validation with the business.
Data exploration and visualization to understand and define features for a given data set.
Data model training and tuning to reduce errors and increase reliability and accuracy.
Participate in innovation forums to identify new ways to leverage data to solve business issues.