7 hours ago

Data Engineer - Emergency Preparedness and Response (Detroit, MI, US)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Build and optimize enterprise data pipelines that automate collection, transformation, storage, delivery, and reporting to support downstream analytics and operational decision-making. Partner with business units, data architects, cloud engineers, and data scientists to design data structures, improve data quality, and prototype proof-of-concepts. Develop automated datasets for process improvement metrics, implement ETL pipelines across on-prem and cloud, and create reporting/visualizations for compliance and efficiency insights, including deployment of ML models for response use cases.

Location: Detroit

Workplace: Hybrid

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Facilitate data engineering projects and collaborate with stakeholders to develop end-to-end solutions for downstream analytics, machine learning modeling, feature engineering, prototypes, and reporting.
•Work with business units and technical teams to identify relevant data, assess data quality, define data requirements, and develop proof-of-concept prototypes.
•Develop datasets and automated pipelines supporting process improvement and operational efficiency metrics.
•Design and implement data process pipelines to extract, transform, and load data from multiple sources across on-premise or cloud platforms.
•Build reporting and visualizations using data pipelines, and design/implement automation and testing strategies, including deploying machine learning models with orchestration and scheduling.

Key Requirements

•Bachelor’s degree in a quantitative field (e.g., Computer Science, Mathematics, Physics, Data Science, Econometrics) plus 3 years’ experience in data engineering, data analytics, or computer programming; OR Master’s degree with quantitative coursework plus 1 year of experience in the same areas.
•Intermediate proficiency in SQL, including query optimization and basic data model/schema design.
•Intermediate programming skills in a modern language such as Python, C#, Java, or R to support data transformation and automation.
•Experience designing and implementing data process pipelines/ETL in on-premise or cloud environments, including cloud-based platforms such as Azure services (e.g., Data Factory, Databricks).
•Experience with analytics/reporting using BI and data visualization tools such as Power BI / Microsoft Power Platform (and related data blending).

Experience:EnergyUtility

Skills:CollaborationCommunicationAnalytical thinkingProblem-solvingPlanning

Tech Stack:SQLAzureData FactoryDatabricksSQL serverHiveSparkPower BIMicrosoft Power PlatformPythonC#JavaR

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

DTE Energy

DTE Energy is a diversified energy company that generates, transmits, and distributes electricity and natural gas to customers in Michigan. It also invests in renewable energy and energy infrastructure services.

Industry: Utilities

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Detroit, United States

Founded: 1995

Website LinkedIn

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DTE Energy

7 hours ago

Data Engineer - Emergency Preparedness and Response (Detroit, MI, US)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Location: Detroit

Workplace: Hybrid

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Facilitate data engineering projects and collaborate with stakeholders to develop end-to-end solutions for downstream analytics, machine learning modeling, feature engineering, prototypes, and reporting.
•Work with business units and technical teams to identify relevant data, assess data quality, define data requirements, and develop proof-of-concept prototypes.
•Develop datasets and automated pipelines supporting process improvement and operational efficiency metrics.
•Design and implement data process pipelines to extract, transform, and load data from multiple sources across on-premise or cloud platforms.
•Build reporting and visualizations using data pipelines, and design/implement automation and testing strategies, including deploying machine learning models with orchestration and scheduling.

Key Requirements

•Bachelor’s degree in a quantitative field (e.g., Computer Science, Mathematics, Physics, Data Science, Econometrics) plus 3 years’ experience in data engineering, data analytics, or computer programming; OR Master’s degree with quantitative coursework plus 1 year of experience in the same areas.
•Intermediate proficiency in SQL, including query optimization and basic data model/schema design.
•Intermediate programming skills in a modern language such as Python, C#, Java, or R to support data transformation and automation.
•Experience designing and implementing data process pipelines/ETL in on-premise or cloud environments, including cloud-based platforms such as Azure services (e.g., Data Factory, Databricks).
•Experience with analytics/reporting using BI and data visualization tools such as Power BI / Microsoft Power Platform (and related data blending).

Experience:EnergyUtility

Skills:CollaborationCommunicationAnalytical thinkingProblem-solvingPlanning

Tech Stack:SQLAzureData FactoryDatabricksSQL serverHiveSparkPower BIMicrosoft Power PlatformPythonC#JavaR

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

DTE Energy

Industry: Utilities

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Detroit, United States

Founded: 1995

Website LinkedIn