Senior Data Analyst

Job Title: SENIOR DATA ANALYST

Job Location: Downtown Detroit (Minimum of 3 days required in the office – Tuesday-Thurs)

Duration: 1 year contract with extensions or opportunity to go perm

Qualifications:

Education: Bachelor’s degree in computer science, Mathematical, or STEM related

Experience: 5-7 years minimum in a Data scientist, Data Engineer, or Business Data Analyst position

Tools/Technologies: Python, SQL, Java, Microsoft Power Platform, Power BI, SAP Business Intelligence

Strong Data scientist background with ability to understand regression modeling (linear regression model)

Nice to Haves:

Advanced-level programming skills in structured query language (SASS)

• 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

Job Overview:

DTE Energy is seeking a Senior Data Analyst to join the Analytics team in Downtown Detroit. 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.