Manager Data Engineering

Norwegian Cruise Line International•Miami, FL

About The Position

The Manager of Data Engineering is tasked with leading a team dedicated to data warehousing and supporting legacy integrations and data applications. This role focuses on the development and implementation of data engineering solutions that enhance data warehousing capabilities and maintain legacy systems. This role involves balancing technical project delivery with strategic business alignment, managing team dynamics, and integrating modern data warehousing practices with existing legacy systems. The Manager has autonomy in project management and team leadership, with critical decisions escalated to the Senior Manager of Data Engineering. Challenges include adapting to evolving data technologies, ensuring project efficiency, and maintaining legacy systems without disrupting current operations. Regular communication with the department senior leadership, team members, and other internal departments is essential for effective project execution and team collaboration.

Requirements

Minimum of 7 years’ experience developing, validating, and implementing data warehouses, data systems or cloud-based data solutions.
2+ years of experience managing team.
Experience in data engineering with a focus on data warehousing and legacy systems.
Experience with multiple data modeling approaches, SQL, and scripting programming languages like Python and PowerShell.
Experience with Agile methods & tools like JIRA.
Experience leading and coaching a team members.
Proven experience in data warehousing technologies, legacy system management, strong communication, and stakeholder engagement abilities.
Strong listening, presentation, and interpersonal skills.
Excellent analytical, problem-solving, verbal, and written communication skills.
Able to prioritize and execute tasks in a high-pressure, fast-paced environment.
Excellent decision-making abilities under uncertainty and ability to manage multiple priorities and deadlines for a team.
Ability to drive results independently.
Proven success with collaboration between cross functional teams.
Demonstrated understanding of agile methodologies, QA practices and user experience, with strong digital & technical skills.
Consensus and diplomacy skills with ability to handle varying differences in opinions.
Experienced with collaboration tools (Jira, Confluence, Lucid Chart).
Strong understanding of databases such as Microsoft SQL Server, Oracle, MySQL, HANA, Snowflake.
Experience with traditional on-prem virtual and physical infrastructure, as well as cloud-based environments, preferably AWS.
Excellent understanding of Windows, and Linux based infrastructure.
Working knowledge of various tools, open-source technologies, and cloud services.

Nice To Haves

Experience with SaaS based solutions and hands on experience on Snowflake, Oracle, SQL server.
Also has good exposure on dbt Matillion and any other ELT / ETL tools.
Innovation, Process Improvement, Creativity, and Clean-Sheet Reinvention.

Responsibilities

Lead the development and maintenance of data warehousing solutions.
Manage legacy integrations and data application enhancements.
Direct and mentor a team of data developers and engineers.
Ensure team alignment with project goals and company objectives.
Lead architecture decisions and able to manage vendor partners.
Work with Product Owners, DevOps and Prioritize activities and manage delivery through Agile methodologies.
Provide transparency through reporting on backlog, data quality metrics, and platform health.
Manage the execution of data engineering projects ensuring they meet defined objectives.
Collaborate with cross-functional teams for integrated project delivery.
Provide technical expertise in data warehousing technologies and legacy systems.
Stay updated on industry trends to guide the team effectively.
Work closely with the Senior Manager and other stakeholders to align data engineering efforts with business needs.
Communicate project progress and challenges effectively.
Ensure adherence to best practices in data governance and security.
Maintain high standards of quality in data engineering outputs.
Perform other job-related functions as assigned.