Manager Data Engineer / Principal Data Engineer

This role is ideal for a strong technical leader with hands-on experience delivering cloud-based data platforms, scalable data pipelines, and enterprise data solutions that support analytics, reporting, data science, and AI use cases. This role will be responsible for creating, maintaining, and optimizing enterprise data pipelines and related processes that support strategic analytics, machine learning, and AI-driven business capabilities across Insurance line of business.

Our client, a IT Services and Consulting company, is looking for a Manager Data Engineer / Principal Data Engineer for their Detroit, MI/ Hybrid location.
 
Responsibilities:

• Client is seeking a Manager Data Engineer / Principal Data Engineer to help design, build, and evolve modern data solutions within our Enterprise Data and AI organization.
• This role is ideal for a strong technical leader with hands-on experience delivering cloud-based data platforms, scalable data pipelines, and enterprise data solutions that support analytics, reporting, data science, and AI use cases.
• The ideal candidate will bring experience implementing data lakes, cloud data warehouses, and modern data engineering solutions, including data ingestion, transformation, standardization, preparation, data quality management, and structured and unstructured data processing.
• This role also requires experience with relational and NoSQL data technologies, large-scale data integration, API development, and enterprise data modeling, including Data Vault.
• Experience with modern cloud data platform technologies, data architecture, and software engineering practices is essential.
• Knowledge of AI/ML, data science, and LLM-related use cases is also a plus.
• Knowledge of the insurance business data domain is a significant advantage, particularly in supporting solutions that enable Insurance Data and Analytics capabilities.
• This role will be responsible for creating, maintaining, and optimizing enterprise data pipelines and related processes that support strategic analytics, machine learning, and AI-driven business capabilities across Insurance line of business.
• The selected candidate will help build and advance critical platform capabilities, including Insurance Data and Analytics solutions, data ingestion and transformation frameworks, governance-aligned data engineering processes, and trusted data foundations for analytics and AI.
• The ideal candidate will bring proven experience leading cloud data engineering initiatives, driving scalable solution design, and establishing strong engineering standards across complex enterprise environments.
• Lead and manage the Data Engineering squad, focusing on scalable platforms and engineer growth.
• Design, build, and optimize modern data pipelines and enterprise data solutions that support analytics, reporting, data science, and AI use cases.
• Develop and implement cloudbased data engineering solutions, including data lakes, cloud data warehouses, and enterprise data platforms.
• Design scalable data structures and support enterprise data storage using Data Vault modeling to enable flexible, auditable, and resilient data solutions.
• Build and support data ingestion, transformation, preparation, and quality processes across structured and unstructured data sources.
• Develop robust solutions for largescale data integration, relational and NoSQL data processing, and APIbased data services.
• Enable trusted, highquality, and accessible data for analytics, machine learning, and AIdriven business capabilities.
• Partner with business and technology teams to define data requirements, transformation rules, integration needs, and solution designs that align to enterprise and lineofbusiness priorities.
• Help advance Insurance Data and Analytics platform capabilities through scalable engineering, strong data foundations, and governancealigned practices.
• Establish and maintain data quality, validation, monitoring, metadata, and lineage processes to support reliable and wellmanaged enterprise data assets.
• Apply modern software engineering methods and Agile practices to deliver scalable, reliable, and maintainable data solutions.
• Promote engineering, operational, and design standards across data platforms and services.
• Evaluate and recommend tools, technologies, and approaches that improve performance, delivery, and longterm supportability.
• Collaborate across business and IT teams to solve complex technical challenges and deliver practical, scalable solutions.
• Support enterprise data governance, data management, and data security requirements.
• Provide technical leadership, mentor team members, and contribute to strong delivery outcomes through effective collaboration and sound engineering practices.

 
Requirements:

• Strong technical expertise in cloud data platforms and modern data engineering tools, combined with team leadership, strategic thinking, and crossfunctional collaboration.
• Bachelor’s degree in computer science, Information Systems, or a related field; advanced degree preferred.
• 8+ years of progressive experience in data engineering, including deep expertise in analyticsfocused data warehouse environments such as Snowflake.
• Extensive handson experience designing and delivering cloudbased data solutions on AWS, including services such as S3, AWS CLI, Lambda, and DynamoDB.
• Strong experience with modern data engineering and integration tools such as dbt, Qlik Replicate, InfoSphere DataStage, and CP4D.
• Deep expertise in data modeling, including Data Vault, and in designing scalable, auditable, and resilient data structures that support enterprise analytics, reporting, and AI use cases.
• Proven experience architecting and optimizing largescale data ingestion, transformation, cleansing, standardization, and deduplication processes across complex data environments.
• Strong programming and automation skills in Python and other scripting languages used to support enterprise data engineering solutions.
• Demonstrated experience leading the design and implementation of enterprise data pipelines using Git, DevOps practices, and modern software engineering approaches.
• Strong background in largescale data integration, API development, and data migration across distributed systems and platforms.
• Deep understanding of data governance, data management, metadata, lineage, and data security, with the ability to embed these practices into engineering solutions.
• Proven ability to lead endtoend solution delivery, working independently while providing technical direction across multiple initiatives and technologies.
• Demonstrated success establishing and enforcing engineering, design, and operational standards across teams and platforms.
• Ability to evaluate emerging tools, technologies, and architectural approaches, and recommend scalable solutions that improve performance, maintainability, and longterm supportability.
• Proven ability to influence, advise, and partner effectively with senior business and technology stakeholders to identify strategic challenges, assess options, and recommend solutions.
• Strong track record of mentoring engineers, promoting engineering excellence, and contributing to highperforming teams.
• Extensive experience across the full software development lifecycle, including architecture, design, implementation, testing, deployment, and operational support.
• Knowledge of the insurance business data domain is a major plus, including familiarity with insurance data concepts, business processes, and analytics use cases.
• Ability to design trusted, highquality data foundations that enable advanced analytics, machine learning, and AIdriven business capabilities.
• Strong problemsolving, communication, and leadership skills, with the ability to translate complex technical concepts into practical business solutions.
• Insurance Business data domain knowledge.
• Years of Experience:
• 12.00 Years of Experience

 
Why Should You Apply?

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities