Data Engineering Manager

Urban Science•Detroit, MI

3d•Hybrid

About The Position

We are Automotive Business Scientists. We empower our clients to turn overwhelming industry data into discovery, action, and measured success. We are unique market leaders because we find and examine possibilities through the clarity of a scientific lens. To solve the toughest client challenges, we need curious, creative, and dedicated people to join our team. We search out individuals who align with our core values and who adhere to the highest standards of integrity and ethics in everything they do. Our company is filled with the brightest minds and the biggest hearts at every level. We recognize that personal success takes on many different forms of the course of our lives – both professional and personally – so we provide a myriad of benefits and programs focused on Wellbeing, Growth, Community and Recognition. One size does not fit all, so we encourage every Urban Scientist to discover their own formula for success including hybrid and remote work options. If this sounds like the kind of company you would like to work with, Apply Now! POSITION OVERVIEW The Data Engineering Manager is a senior technical and people leadership role within Global Data Services (GDS). This position is responsible for leading data engineering teams, owning critical data platforms and processing components, driving operational excellence, and establishing best practices for data architecture, software engineering, and data management. This role combines strategic leadership, technical expertise, operational accountability, and innovation to ensure Urban Science delivers reliable, scalable, secure, and high-quality data solutions that support products, client systems, analytics, and business operations. This role has current Hybrid Workplace flexibility local to our Detroit, MI office location. Candidates must be available and willing to work in-person two days per week. URBAN SCIENCE DOES NOT AND WILL NOT PROVIDE IMMIGRATION RELATED SPONSORSHIP FOR THIS ROLE, NOW OR IN THE FUTURE

Requirements

Proven experience leading technical projects and mentoring engineers.
Ability to communicate effectively with technical teams, business stakeholders, and senior leadership.
Strong collaboration and relationship-building skills across global organizations.
Strong experience with SQL and relational database technologies, particularly Microsoft SQL Server.
Deep understanding of data modeling, ETL/ELT, data integration, and modern data architectures.
Knowledge of structured, semi-structured, and unstructured data platforms.
Experience with distributed systems, APIs, microservices, and enterprise integration patterns.
Hands-on experience with programming/scripting languages such as Python or C#.
Strong understanding of software engineering best practices, CI/CD, testing, source control, and operational support.
Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience.
8-10+ years of experience in data engineering, software engineering, or related technical roles.
Experience leading, projects, or major technical initiatives.
Experience supporting large-scale data processing and analytics environments.
2+ years in data-related background

Nice To Haves

Data Analytics experience is a plus

Responsibilities

Lead, mentor, and develop Data Engineering team members.
Manage performance, career development, succession planning, and staffing needs.
Foster collaboration across Technology, Product, Analytics, Practice, and Client-facing teams.
Provide technical leadership and establish engineering best practices across the organization.
Communicate team performance, priorities, risks, and KPIs to leadership.
Design, build, and maintain scalable data pipelines, platforms, and processing systems.
Own and evolve one or more critical data processing capabilities within GDS.
Define data architecture standards and ensure solutions support business, product, and analytics requirements.
Integrate data from diverse internal, client, and third-party sources.
Apply software engineering, distributed systems, and data management best practices to all solutions.
Ensure reliable, efficient, and sustainable day-to-day data operations.
Oversee production processing, incident management, support processes, and service quality.
Standardize tooling, processes, and documentation to improve scalability and reduce operational risk.
Manage data migrations, troubleshooting, profiling, anomaly detection, and data quality initiatives.
Ensure teams are properly resourced to support current operations and future growth.
Identify opportunities to improve data quality, reliability, performance, and cost efficiency.
Lead proof-of-concept initiatives and evaluate emerging technologies, tools, and methodologies.
Drive automation and modernization efforts across data platforms and processes.
Contribute to technology strategy, architectural decisions, and enterprise-wide engineering practices.
Ensure compliance with security, privacy, contractual, and regulatory requirements.
Define and support data governance, access control, and operational policies.
Promote sustainable, documented, and auditable data management practices.
Champion data confidentiality and risk management across all data operations.