Manager - Data Integrity and Analytics

DTE Energy•Detroit, MI

3d•Hybrid

About The Position

The Manager - Data Integrity and Analytics leads a team of individual contributors responsible for initiatives and projects involving data migration, master data management, data clean up, data governance, and data quality. The role provides leadership and guidance to analytical professionals on data definitions, expectations, and compliance with project initiatives. It partners with business leaders to define performance measures, identify critical priorities, and develop business plans. The manager establishes strategies for data clean up, maintenance, and quality control; oversees the integrity of data used to analyze business operations and effectiveness; and advances the analytics discipline through technology evaluation, experimentation, improved methodologies, and organizational learning. The role works with senior leaders to identify business intelligence projects with measurable value and translates strategies into short- and long-term projects, overseeing execution from end to end. It also carries supervisory and management responsibilities, including planning, staffing, coaching, performance management, coordination, reporting, and budgeting, and leads continuous improvement and staff development. This is a hybrid role with an established schedule of in-person work at an assigned work location. Remote work is expected to take place from the employee’s primary residence, subject to company guidelines. The role must be available for a primary assignment supporting DTE’s emergency response to storms or other events affecting customer service.

Requirements

Bachelor’s degree in business administration, data analytics, economics, information technology, engineering, or a related field.
Seven years of experience in data management, engineering, operations, or information technology, including five years of formal or project leadership experience.
Knowledge of data science languages such as Python, R, Java, PHP, or SQL.
Familiarity with major subjects, technologies, and solutions in predictive analytics.
Understanding of Big Data and cloud-based data architecture.
Strong experience leading analytical data projects across functional teams, including coaching and providing feedback on analytical approaches.
Excellent communication skills, including the ability to interact with and influence decision-making by non-analytical business audiences.
Experience in workforce planning, team engagement, budget planning, contract negotiations, and vendor management.
Experience with data analysis, quality, cleaning, and extraction or validation tools.
Knowledge of data profiling, root cause analysis, and quality improvement.
Ability to produce and communicate data insights that translate into significant business impact.
Ability to write code to query and transform both unstructured and structured data.

Nice To Haves

Master’s degree in business administration, data analytics, economics, information technology, engineering, or a related field.
Three years of experience with quantitative analysis, data queries, data visualization, statistical analysis, and predictive analytics.

Responsibilities

Define and establish the overall management strategy and approach to data clean up, maintenance, and quality control.
Advance analytics practices by evaluating new technologies, experimenting with methodologies, improving techniques, and fostering a learning culture.
Oversee the integrity of data used to analyze business operations and operational effectiveness.
Collaborate with senior leaders to identify business intelligence projects with measurable value propositions that improve performance, reduce costs, increase efficiencies, or create business opportunities.
Translate business and analytics strategies into short- and long-term projects and manage their end-to-end execution.
Perform supervisory and management functions, including planning, organizing, directing ongoing activities and special assignments, staffing, employee selection, training, coaching, performance management, time entry, coordinating, reporting, and budgeting.
Lead continuous improvement efforts by designing processes, establishing quality and quantity standards and metrics, sharing best practices and knowledge, developing staff, and using process-design outcomes to solve problems.
Provide leadership and guidance to analytical professionals on data definitions, expectations, and compliance with project initiatives.
Partner with business leaders to define performance measures, identify critical priorities, and develop business plans.
Mentor team members on coding to query and transform structured and unstructured data.