Manager Data Engineering

INTRODUCTION:

WHY JOIN US?

ROLE DESCRIPTION

RESPONSIBILITIES

• Lead architecture and design reviews, and technical discussions for data engineering initiatives.
• Define and drive engineering standards, design principles, reusable frameworks, and establish best practices.
• Provide technical leadership across Azure Data Platform, ADF, Databricks, Lakehouse Architecture, Data Integration, and Analytics solutions.
• Evaluate architectural alternatives and technology choices based on scalability, reliability, maintainability, security, and cost optimization.
• Act as the senior technical escalation point for complex engineering challenges and critical delivery decisions.
• Own end-to-end delivery of Data Engineering, Analytics, Data Platform Modernization, and AI-enablement initiatives.
• Partner with Product Owners and business stakeholders to translate business objectives into technical roadmaps and execution plans.
• Drive effort estimation, work breakdown structures, milestone planning, dependency management, and resource planning.
• Ensure delivery commitments are achieved with the expected quality, timeline, and business outcomes.
• Lead and mentor a team of Data Engineers, fostering a culture of engineering excellence, ownership, innovation, and continuous improvement.
• Build strong partnerships between business and engineering teams to ensure successful delivery outcomes.

EDUCATION AND EXPERIENCE

• Bachelor’s degree in technology or engineering is required.
• 12-15 years of overall experience in Data & Analytics, including 5+ years of hands-on experience with Azure data engineering technologies.
• At least 3+ years of experience in leading data engineering teams or working in similar technical lead roles.
• Strong experience in architecting and designing scalable, secure data solutions and building AI-ready data platforms.
• SKILL REQUIREMENTS
• Strong hands-on experience with Azure Data Factory (ADF), Databricks, PySpark & SQL.
• Strong understanding of Databricks Unity Catalog and CI/CD using Azure DevOps.
• Proven experience in designing secure, scalable, and AI‐ready data solutions.
• Ability to collaborate with business and technical stakeholders to gather requirements and deliver high‐impact data solutions.
• Strong sense of ownership, accountability, and execution discipline across multiple initiatives.
• KEY COMPETENCIES
• Excellent communication skills, with the ability to handle high-impact business stakeholder interactions effectively.
• Analytical thinking and judgment to evaluate complex problems and make sound architectural and design decisions considering scalability, security, and business impact.
• Strong leadership and ownership, with the ability to mentor team and drive execution excellence and continuous improvement.
• KEY SKILLS FUNCTIONAL COMPETENCIES (Mandatory)
• Data Engineering & Architecture - Designs and delivers scalable, secure, and AI‐ready data solutions, including batch and streaming data ingestion.
• Azure & Databricks - Strong hands‐on experience with Azure data services, ADF, Databricks, Unity Catalog, PySpark and SQL.
• Stakeholder Collaboration - Effectively partner with Business, Architecture, Data science, and Product teams to translate requirements into impactful data solutions.
• Join our Talent Community to stay connected with us!
• Qnity is an equal opportunity employer. Qualified applicants will be considered without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability or any other protected class. If you need a reasonable accommodation to search or apply for a position, please visit our Accessibility Page for Contact Information.
• Qnity offers a comprehensive pay and benefits package. To learn more visit the Compensation and Benefits page.
• We use Artificial Intelligence (AI) to enhance our recruitment process.

SKILL REQUIREMENTS

KEY COMPETENCIES

KEY SKILLS FUNCTIONAL COMPETENCIES (Mandatory)

Preferred Qualifications

• Relevant certifications in Azure, Databricks, Data Engineering, AI, or Data Governance are preferred.