Senior Data Engineer/ Principal Data Engineer

Job Title: Senior Data Engineer/ Principal Data Engineer
Work Location: Jackson, MI(2 days onsite)
Duration :: Fulltime

If you find this opportunity suitable, kindly share your updated resume. Also, please let me know a good time to connect with you for a quick discussion.

Looking forward to hearing from you!

Job Description:

If you're looking for a career that will provide a challenging work environment and many opportunities to learn, our company is the place for you. Join our team and become part of a company full of exciting opportunities. We help employees achieve their personal best by offering many opportunities that develop and enhance their skills for career advancement.

General Summary of Responsibilities:

• The Applications and Analytics team is the heart of innovation, powering Michigan s future by crafting cutting edge digital solutions that transform our customers and communities experience energy.
• Guided by our digital first strategy, we harness modern technical practices and agile methodologies to ensure data and insights are accessible, reliable and actionable, enabling evidence based decision making, operational efficiency and innovation across the organization. Join a dynamic team where collaboration, creativity and impact converge.
• We partner closely with business teams to uncover digital opportunities, prioritize high impact outcomes and deliver trustworthy, user-friendly technology. B
• y embracing our CE way principles- engaging on solutions, illuminating priorities and minimizing waste we build systems that are simple, helpful and valuable.
• If you re passionate about innovation, community and shaping a sustainable energy future, come grow with us at company.
• The Principal Data Engineer is responsible for partnering with diverse analytics teams and business stakeholders across the enterprise design, develop and maintain data pipelines and architecture aligned with established IT best practices.
• Key responsibilities include developing data warehouses, designing, extracting, loading and testing data solutions performing data modeling, ensuring application stability assembling and cleansing large datasets creating and optimizing ETL processes; and identifying opportunities to automate manual data processes.
• This role also leads and provides guidance to lower-level Data Engineers, establishing team standards, frameworks, and best practices while ensuring adherence to data engineering principles across analytics teams.

Essential Duties and Responsibilities :

• Assemble and cleanse large datasets from various sources to support business requirements for enhancements of varying complexity.
• Capture and evaluate requirements from data architects and business partners, assess development alternatives, and establish clear delivery timelines.
• Create data visualizations to support customer insights, operational efficiency, and other key business performance metrics using a variety of reporting tools.
• Identify opportunities to improve internal processes by automating manual data wrangling, optimizing performance, and enhancing data quality.
• Prepare documentation and artifacts to support solution delivery and project activities.
• Provide day-to-day guidance and mentorship to lower-level Data Engineers to support talent development within the department.
• Deliver incident management and technical consulting for current applications and solutions.
• Provide technical direction for enhancements of varying scope, including solution alternatives, design, testing, and documentation.
• Collaborate with stakeholders and IT partners to design and build efficient data pipelines supporting business and technical requirements.

Knowledge/Skills/Abilities:

• Ability to work effectively with contract employees and external vendors.
• Knowledge of change and release management tools and processes used to implement solutions across multiple teams and technologies.
• Strong understanding of data design principles and systems engineering concepts, including structured design, supportability, scalability, reliability, survivability, and maintainability.
• Knowledge of project planning and full lifecycle delivery using Agile methodologies.
• Excellent verbal and written communication skills, with the ability to work effectively at all levels of the organization.
• Strong ability to establish and maintain effective working relationships across business and IT teams.
• Understanding of data testing methodologies and associated tools.
• Knowledge of database management principles, including data structures, data modeling, data warehousing, and transaction processing.

Preferred Experience:

Candidates with one or more of the following skills will stand out:

• Ownership of enterprise-level lakehouse architecture, including defining reference architectures, patterns, and guardrails for Databricks platforms at scale.
• Recognized expertise in Databricks platform strategy, including governance, workspace/domain strategy, standardization, and team enablement.
• Mastery of analytics data modeling standards, including fact/dimension strategies, conformed dimensions, SCD patterns, bridges, and metric governance.
• Proven ability to create and enforce engineering standards (e.g., dbt conventions, testing frameworks, documentation standards, code review practices).
• Expert-level proficiency in Spark and distributed compute optimization, including guiding teams on design tradeoffs and performance best practices.
• Strong experience designing secure, discoverable, self-service data products using catalog, lineage, and access governance principles.
• Ability to shape cross-team roadmaps and influence leaders through architecture reviews, decision records, and executive-level recommendations.
• Demonstrated leadership in platform reliability and operational excellence, including observability standards, incident response frameworks, resilience practices, and cost optimization.
• Experience enabling modern analytics and machine learning practices, including feature-ready datasets, MLflow/MLOps alignment, and experimentation/production workflows.
• Recognized ability to mentor and coach technical talent, elevate engineering practices, and scale best practices across an organization.