Sr. Manager, Data Engineering

As a Sr. Manager, Data Engineering, you will lead the development and evolution of data platforms, analytics capabilities, and engineering practices that support business growth. You will drive data strategy, mentor technical teams, and collaborate with stakeholders to deliver impactful data products.

• Own and execute the data engineering and analytics roadmap, aligning priorities with business goals and organizational needs.
• Develop and promote a data-as-a-product mindset by creating scalable, accessible, and well-governed data solutions.
• Lead, mentor, and grow a team of data engineers and analysts, fostering technical excellence and continuous development.
• Design and implement modern data strategies, architectures, and engineering practices across the organization.
• Build foundational dashboards, analytics solutions, and BI capabilities that empower teams to make data-driven decisions.
• Partner with marketing, product, engineering, and executive stakeholders to deliver high-impact data products.
• Present data strategies, insights, and recommendations to technical and non-technical audiences.
• Establish strong data governance, security, lineage, and quality standards.
• Collaborate on machine learning systems, automation initiatives, and data enrichment capabilities.
• Evaluate and introduce new data sources, tools, and technologies to improve analytics maturity.
• Stay current with advancements in data engineering, machine learning, analytics, and industry trends to guide strategic decisions.

Requirements:

The ideal candidate is an experienced data engineering leader with a strong background in building scalable data platforms, managing technical teams, and applying advanced analytics to solve business challenges. You should have strong technical depth combined with excellent communication and leadership skills.

• Bachelor’s or advanced degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 5–8+ years of experience in Data Engineering, including at least 3 years in a leadership or management role.
• Proven experience leading and developing high-performing technical teams.
• Strong expertise in machine learning, statistical modeling, and advanced analytics techniques.
• Strong programming skills in Python, R, or similar languages.
• Experience with data science and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience with big data technologies such as Hadoop, Spark, Snowflake, or similar platforms.
• Experience working with cloud platforms including AWS, Google Cloud, Azure, or equivalent technologies.
• Strong understanding of data architecture, engineering best practices, governance, and security.
• Ability to translate complex technical concepts into clear business insights for non-technical stakeholders.
• Strategic mindset with the ability to manage multiple priorities in a fast-paced environment.
• Collaborative leadership style focused on mentoring, inclusion, and team development.
• Strong business acumen and understanding of how data solutions drive organizational outcomes.
• Willingness to work in a hybrid/on-site environment according to business requirements.

Benefits:

• Competitive compensation package aligned with skills, experience, and market standards.
• Opportunity to lead impactful data transformation initiatives within a global technology environment.
• Professional development opportunities and continuous learning programs.
• Employee recognition initiatives celebrating individual and team achievements.
• Exposure to global teams and collaboration across multiple regions.
• Team offsites and opportunities to build strong professional relationships.
• Lifestyle spending account supporting wellness and work-from-home needs.
• Culture built around accountability, respect, integrity, innovation, and collaboration.
• Opportunities for career growth and technical leadership development.
• Inclusive workplace environment focused on belonging and employee growth.