Job Description

STRATEGIC STAFFING SOLUTIONS (S3) HAS AN OPENING!

Title: Data Engineer

Location: Detroit, MI Hybrid Local - In office (Tue, Wed, Thu)  

Duration: 12 Months
Role Type: W2 contract engagement

Job Summary 

Leads data integration and analytics projects that support data collection, automation, transformation, storage, delivery, and reporting processes. Serves as senior advisor for large business unit or enterprise-level data projects. Optimizes data retrieval and processing, including performance tuning, delivery design for down-stream analytics, machine learning modeling (including feature engineering), and reporting. Mentors less-experienced team members Span of control: 0; individual contributor. 
  
Key Accountabilities 

•     Leads data engineering projects and collaborates with stakeholders to formulate end-to-end solutions, including data structure design to feed downstream analytics, machine learning modeling, feature engineering, prototype development, and reporting 
  •    Develops complex data sets and automated pipelines that support data requirements for process improvement and operational efficiency metrics 
  •    Designs, implements and maintains data process pipelines in on-premises or Cloud platforms required for optimal extraction, transformation, and loading of data from multiple data sources 
  •    Builds reporting and visualizations that utilize data pipeline to provide actionable insights into compliance rates, operational efficiency, and other key business performance metrics 
  •    Designs and implements effective testing strategies for data pipelines and processing methods 
  •    Deploys and automates Machine Learning Models in a data environment (e.g., SQL server, Cloud platform, on-premise servers and machines), including workflow orchestration, scheduling and advanced data processing implementation, and data delivery tools 
  •    Educates leaders and other employees on complex data and analytical findings in basic terms and with storytelling and data visualization 
  •    Researches and maintains industry best practices for data engineering practices and solutions 

Minimum Education & Experience Requirements

This is a multi-track base requirement job; education and experience requirements can be satisfied through one of the following three options: 

• Bachelor’s degree with emphasis on coursework of a quantitative nature (e.g., Computer Science, Mathematics, Physics, Data Science, Econometrics, etc.) and 8 years of experience working in a data engineering, data analytical or computer programming function; OR 

•  Master’s degree with emphasis on coursework of a quantitative nature (e.g., Computer Science, Mathematics, Physics, Data Science, Econometrics, etc.) and 6 years of experience working in a data engineering, data analytical or computer programming function: OR 

• Ph.D. degree with emphasis on coursework of a quantitative nature (e.g., Computer Science, Mathematics, Physics, Data Science, Econometrics, etc.) and 4 years of experience working in a data engineering, data analytical or computer programming function. Other Qualifications 

 Preferred: 

• Business domain knowledge 

• Database design and query optimization experience 

• Knowledge of implementing and maintaining feature stores 

• Utility / Energy or customer-oriented industry experience 

• Experience with SAP Business Intelligence Tools (e.g., Business Objects (BOBJ) and SAP Customer Relationship and billing front and back end, and reporting systems (e.g., SAP CRM, SAP I-SU, and SAP BW data) 

•  Familiarity with Continuous Improvement (CI) concepts and applications 

• Strong written and verbal communication skills 

 Other Qualifications

•     Advanced-level programming skills in structured query language (e.g., SQL, Java, Python, etc.) 
  •    Advanced ability in articulating business questions and pulling data from relational databases 
  •    Advanced-level proficiency in business intelligence tools and data blending tools (e.g., Microsoft Power Platform, Power BI, etc.) 
  •    Advanced-level proficiency with Big Data platforms, including data extraction and connection to platforms for analytics 
  •    Ability to adapt to software platforms for retrieving, analyzing, sharing, and visualizing data 
  •    Ability to work with Data Scientists to understand statistical and machine models and optimize data engineering solutions accordingly 
  •    Ability to identify optimal analytical tools based on problem requirements 
  •    Analytical, problem solving, planning, and decision-making skills, with ability to identify key issues from a broad range of alternatives and recommend optimal solutions 
  •    Advances self and other’s knowledge in business processes, new analytical frameworks, and data-driven technologies and applications 
  •    Ability to communicate technical information and complex data analytics to a non-technical audience in a clear and concise manner (in both verbal and written form)