Lead Business Intelligence Analyst

Caterpillar · Industrial · Chicago, IL

Lead Business Intelligence Analyst at Caterpillar, focusing on data quality, development, and deployment of digital platforms. Responsibilities include programming, technical guidance, problem resolution, and driving application development for business features. Requires a Master's degree in a related field and experience in data processing, ML frameworks, Python, NoSQL, relational databases, integrating analytical models, statistical methods, AWS services, data warehousing, and BI tools.

What you'd actually do

  1. Contribute to the design, development, deployment, and quality of Caterpillar’s state-of-the-art digital platform by leading the development of advanced Data Quality methods and routines.
  2. Perform programming and development assignments.
  3. Provide programming and application/technical guidance, and assistance to other team members.
  4. Work directly on complex application/technical problem identification and resolution, including responding to off-shift and weekend support calls.
  5. Work independently on complex systems or infrastructure components that may be used by one or more applications or systems.

Skills

Required

  • Master’s degree, or foreign equivalent, in Data Science, Computer Engineering, Industrial Engineering, or related field
  • 4 years of experience as a Software Developer, Data Engineer, or related occupation
  • Designing and implementing data processing and machine learning frameworks
  • Python
  • NoSQL databases
  • Relational databases
  • Compiling and standardizing diverse and non-sanitized datasets
  • Integrating analytical models with existing data pipelines
  • Statistical approaches
  • Quantitative analytic methods
  • Data management techniques
  • AWS full-stack development
  • AWS Athena
  • AWS Glue
  • AWS DynamoDB
  • AWS EC2
  • AWS EMR
  • AWS RDS
  • AWS S3
  • AWS Sage Maker
  • Data warehouse systems
  • Snowflake
  • Hadoop
  • BI software
  • Tableau
  • MS Power BI

What the JD emphasized

  • Designing and implementing data processing and machine learning frameworks
  • Python, NoSQL, and relational databases
  • Compiling and standardizing diverse and non-sanitized datasets
  • Integrating analytical models with existing data pipelines
  • Statistical approaches, quantitative analytic methods, or data management techniques
  • AWS full-stack development and services such as Athena, Glue, DynamoDB, EC2, EMR, RDS, S3, and Sage Maker
  • Data warehouse systems such as: Snowflake or Hadoop
  • Visualizing data using BI software such as Tableau and MS Power BI