Analytics & Solutions Consultant (HYBRID)

Responsibilities

• Lead day-to-day work to acquire, analyze, combine, synthesize, and structure data with clear definitions and sources for analytical consumption
• Consult with data consumers to identify meaningful datasets for analytical consumption
• Lead day-to-day development of data products using continuous deployment and integration practices
• Apply engineering best practices in order to analyze, design, develop, deploy and support data analytics products
• Participate in agile story authoring, sizing, and demo sessions for product features
• Lead code reviews and provide feedback to the team
• Mentor junior engineers

Requirements

• Bachelor's Degree
• Experience designing and developing solutions using Microsoft Power Platform technologies, including Power Apps, Power Automate, Power BI, Dataverse, and related services.
• At least 4 years of professional experience in data acquisition, cleansing, feature engineering, debugging and software documentation, using modern languages and practices
• Experience using continuous integration and deployment concepts
• At least 3+ year experience with specific technical requirements/platforms will vary (i.e. AWS data science and analytics cloud technologies such as S3, EMR, Spark, PySpark, RDS, JupyterLab, Sagemaker, etc., data warehouses such as Redshift or Snowflake; and digital analytics tools such as Adobe Analytics and Heap)
• Proficiency navigating various types of database models and DBMS’s to create data sets for analytics and model training and development
• Experience developing solutions using agile methods
• Experience working in data lake environments
• Experience guiding the work of junior developers
• Experience with Testing or data accuracy/quality designs
• Advanced programming skills
• Strong Python and cloud compute skills
• Strong SQL skills and background in ETL
• Deep understanding of distributed data systems and applications.
• Proficiency with data lake design and implementation considerations such as columnar storage formats (Parquet) and partitioning
• Deep understanding of analytics and data science applications
• Deep understanding of machine learning systems
• Development of requirement driven data models such dimensional and relational.
• Deep understanding of the business domain and the meaning of the data in it to accurately identify key and associated data which can be extracted, transformed and loaded for effective consumption downstream.
• Develop the data flow so that data can