Data Engineering Manager

At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient’s mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds. As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talent team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels included, respected, empowered and recognised.

What You Can Expect

The Manager – Data Engineering is responsible for leading the design, development, and governance of scalable data solutions across Snowflake and cloud-based environments. This role combines deep technical expertise with strong leadership to guide engineering teams in an Agile environment. The Architect will define architectural standards, review solution designs, mentor engineers, and work closely with cross-functional stakeholders across regions to ensure high-quality, secure, and maintainable data solutions.

Work Location: Bangalore

Work Mode: Hybrid (3 Days in office)

How Youll Create Impact

• Lead and mentor data engineering teams to deliver high-quality, scalable solutions in an Agile setup.
• Define, architect, and govern maintainable, cloud-based data platforms aligned to enterprise standards.
• Oversee solution design across Snowflake, orchestration platforms, ingestion layers, and integration workflows.
• Provide technical leadership on Python automation, data pipelines, and orchestration tools (Airflow, Dagster, etc.).
• Collaborate with product owners, business stakeholders, and engineering leads to translate requirements into technical designs and execution plans.
• Establish engineering standards, coding guidelines, and code review processes to ensure data quality and governance.
• Promote best practices in DevOps, CI/CD, observability, and secure development.
• Support the evaluation and adoption of new technologies and drive continuous improvement within the engineering function.
• Partner with global engineering and architecture teams to ensure alignment across regions and platforms.
  
  

• Proven ability to lead and mentor developers across multiple geographies and teams.
• Strong architectural thinking with the ability to simplify complex problems and communicate clear solutions.
• Ability to navigate ambiguity, provide clarity, and drive structured decision-making.
• Strong analytical, organizational, and communication skills.
• Demonstrated success managing medium- to large-scale data initiatives in Agile environments.
• Ability to build strong relationships with technical and functional partners.
• High adaptability to emerging technologies and evolving business requirements.
• Ability to operate across multiple technology stacks and understand end-to-end impacts.
  
  

• Minimum 3+ years of direct or indirect leadership experience leading developers or Agile teams.
• 5+ years of experience with relational databases and cloud data platforms (Snowflake strongly preferred).
• Strong understanding of data modeling principles and implementation practices.
• Hands-on architectural experience designing scalable, cloud-based data solutions.
• Proficiency in Python and SQL for integration, orchestration, and automation workloads.
• Experience with orchestration frameworks such as Airflow or Dagster.
• Experience interacting directly with business stakeholders and translating functional needs into technical solutions.
• Experience with dbt, Snowflake data modeling, AI/ML workflows, or GxP validation is beneficial.
• Experience handling sensitive data, including PHI, is advantageous.
• Cloud data platforms (Snowflake preferred) and relational databases
• CI/CD platforms (GitHub, GitLab, Azure DevOps), DevOps practices
• Data modeling and SQL-based transformation frameworks (dbt is a plus)
• Cloud ecosystems (AWS, Azure, GCP), cloud storage, and related services
• Git-based version control systems and branching strategies
• Data Visualization (Power BI / Tableau) - Preferred