Engineering Manager, Data Platform and Governance
New
C
CanopyConnected vehicle security
Workable workplace: remote; Workable locations: Detroit, Michigan, United StatesFull-TimeManager
Salary123,800 - 173,100 USD per year
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Job Details
- Experience
- 8+ years of experience in software engineering, data engineering, analytics engineering, data science, or related disciplines; 3+ years of experience leading technical teams.
- Required Skills
- AWSPythonSQLData engineering
Requirements
- Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related technical field.
- 8+ years of experience in software engineering, data engineering, analytics engineering, data science, or related disciplines.
- 3+ years of experience leading technical teams, including hiring, mentoring, performance management, and technical leadership.
- Experience designing and operating modern cloud-based data platforms and data pipelines.
- Strong understanding of data warehousing, data lakes, ETL/ELT architectures, data modeling, and data integration patterns.
- Proficiency with SQL and at least one modern programming language such as Python, Java, or Scala.
- Experience with AWS, Azure, or Google Cloud Platform.
- Experience implementing data governance frameworks, data quality programs, and data lifecycle management practices.
- Working knowledge of privacy and data protection regulations, including GDPR and CCPA.
- Experience building analytics solutions, dashboards, and KPI frameworks that support business decision-making.
- Ability to balance strategic planning with hands-on technical execution.
- Preferred: Master's degree in a related technical field; reside within the Detroit, Michigan area or nearby and be able to work in a hybrid environment and regularly commute to the Detroit office as needed.
Responsibilities
- Define and execute the company's data platform, analytics, and governance strategy.
- Build and lead a team of data engineers, analytics engineers, data scientists, and analysts.
- Establish technical roadmaps, architecture standards, and best practices for data systems and services.
- Lead development of data pipelines, ETL/ELT processes, streaming architectures, and data integration solutions.
- Establish data quality, observability, lineage, cataloging, and reliability practices.
- Develop dashboards, data products, metrics, and KPIs for product, customer, and business outcomes.
- Develop and maintain enterprise data governance policies, standards, and operating procedures.
- Define data ownership, stewardship, retention, classification, and lifecycle management processes.
- Lead privacy, consent management, and regulatory compliance initiatives, and partner with Security, Legal, and Compliance on data controls.
- Establish auditability and data access controls for AI and analytics workloads; support reviews, risk assessments, audits, and customer data inquiries.
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