Manager - Data Engineering
- Compensation
- Not disclosed
- Location
- Tampa, FL, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
- • 10+ years of experience in data engineering, with at least 3 years in a leadership role.
- • Expertise in ETL/ELT tools, data modeling, and cloud platforms (AWS preferred).
- • Strong experience with Spark, Snowflake, Airflow, Talend, and Python.
- • Familiarity with Kubernetes, microservices architecture, and observability tools.
- • Knowledge of Generative AI, ML pipelines, and modern data governance practices.
- • Excellent communication, leadership, and stakeholder management skills.
- • Regular, predictable, full attendance is an essential function of the job
- • Willingness to travel as necessary, work the required schedule, work at the specific location required, complete Penske employment application, submit to a background investigation (to include past
- Physical Requirements:
- • The physical and mental demands described here are representative of those that must be met by an associate to successfully perform the essential functions of this job. Reasonable accommodations may
- • The associate will be required to: read; communicate verbally and/or in written form; remember and analyze certain information; and remember and understand certain instructions or guidelines.
- • While performing the duties of this job, the associate may be required to stand, walk, and sit. The associate is frequently required to use hands to touch, handle, and feel, and to reach with hands
Full description
Position Summary
The Manager – Data Engineering leads the strategic design, development, and governance of enterprise data platforms and solutions that power advanced analytics, business intelligence, machine learning, and Generative AI initiatives. This role is responsible for data architecture, integration, modeling, and platform administration across cloud and on-premise environments. The Manager collaborates with cross-functional teams, drives innovation, and ensures alignment with organizational data strategy and governance standards. Additionally, this position oversees decentralized data lake architecture to enable scalable and flexible data access across business domains.
As a Penske Manager of Data Engineering, you will be responsible for designing and implementing enterprise-level data architectures and analytics systems, and making strategic technology recommendations to drive data-driven decision-making across the organization. You will lead a team of data engineering professionals to establish best practices, develop robust ETL pipelines, and create comprehensive roadmaps for data integration and AI enablement. You will also oversee a diverse portfolio of data engineering initiatives spanning cloud data platforms, real-time data integration, advanced analytics, and AI-ready data solutions, delivering innovative capabilities that unlock the value of Penske's data assets and power next-generation AI applications.
Key ResponsibilitiesLeadership & Strategy
- Lead and mentor a team of data engineers, architects, and analysts to deliver scalable, high-performance data solutions.
- Define and execute enterprise data strategy, architecture, and governance frameworks.
- Collaborate with business stakeholders, product owners, and IT managers to define sprint plans and deliverables using Agile methodologies.
- Drive adoption of Generative AI capabilities for data summarization, metadata enrichment, and intelligent query generation.
Data Architecture & Development
- Oversee design and implementation of logical and physical data models aligned with business requirements and data architecture standards.
- Manage and enhance the Penske Enterprise Data Warehouse supporting domains such as Lease, Rental, Sales, Maintenance, Connected Fleet, Accounting, and Finance.
- Architect and oversee data ingestion, transformation, and engineering pipelines using Talend, SAP BODS, Spark EMR, Snowflake, Airflow, and other tools.
- Develop and maintain Spark-based reusable frameworks for data engineering and data quality validation.
- Design and deploy Python microservices for REST APIs within Kubernetes (K8s) environments.
- Implement observability practices across data platforms to ensure reliability, performance, and traceability of data workflows.