Consulting, Staff

Provide advisory and implementation services of large-scale data ecosystems, including data management, governance and the integration of structured and unstructured data to help companies unlock the value of big technology investments. Work across all phases of the Agile and Waterfall lifecycle for the design, development, testing and implementation of Master Data Management, Data Governance, Data Analytics and Data Integration solutions for enterprise-level clients. Provide technical recommendations for optimized data access and retention from various data warehouses. Implement data integration solutions using dimensional modeling, granularity and source-to-target mapping to integrate new BI/DW requirements. Perform data management, including master data, metadata, data architecture, data governance, data quality, data modeling, etc. Design database queries, triggers, procedures, functions and packages for reporting and data analytics. Design and develop data cleansing routines utilizing typical data quality functions, including standardization, transformation, rationalization, linking and matching. Implement data enrichment, lookup, filtering and data cleansing routines and solutions to improve data quality of the ingested data.

• 10% Travel required nationally.
• Telecommuting permitted.
• Work location includes various unanticipated Deloitte office locations and client sites nationally.

Bachelor's degree or foreign equivalent in Business Administration, any STEM field, or a related field. Must have 1 year of related work experience in the job offered or in a related occupation. Position requires 1 year of related work experience in each of the following:

• Utilizing Big Data technologies, including Jupyter, Apache Spark, and Kubeflow to coordinate and drive aspects of the full data delivery lifecycle for implementations, including requirements gathering, business process analysis, scripting, code deployments, enhancement validation;
• Performing data analysis, including inspecting, cleansing, transforming, and modeling for enabling data mining and resolving data mapping and data modeling issues, using Pandas, NumPy, and Apache Spark;
• Building reports and data models in JupyterHub, AWS SageMaker, and Azure based on performance measurement metrics, allowing for business and technical users to provide insights into the organization's data;
• Developing business solutions leveraging emerging automation, cognitive, and machine learning tools, including scikit-learn, TensorFlow, Keras, H2O, Optuna, Hugging Face Transformers, and Natural Language Toolkit (NLTK);
• Helping to provide industry insight and analytical support using data mining, pattern matching, data visualization, and predictive modeling, using Extreme Gradient Boost (XGBoost), Light Gradient Boosting Machine (LightGBM), CATegorical Boosting, Neural Network, and generalized linear regression models;
• Helping to provide expertise in trend analysis to support diverse business initiatives using time-series modeling tools including Autoregressive Moving Average (ARMA), long short-term memory models, Exponential Smoothing State Space algorithm (ETS) models, and Prophet;
• Designing database queries, triggers, procedures, functions, and packages for reporting and data analytics, using MySQL and PySpark; and
• Develop various statistical testing frameworks for multiple business purposes, including model quality testing, business strategy evaluation, and data drift analysis.