The Data Engineer designs, develops, and maintains reliable data pipelines, transformations, and models to support analytics and operational workflows. They collaborate with cross-functional teams to translate business requirements into secure, scalable, and high-quality data solutions.
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 Data Engineer designs, develops, and maintains reliable data pipelines, transformations, and models that support analytics, reporting, and operational workflows. This is a hands-on individual-contributor role that combines strong SQL and Python development with practical data-engineering experience in cloud environments.
The role works closely with architects, analysts, and business stakeholders to translate requirements into secure, scalable, and high-quality data solutions. The Data Engineer is expected to own assigned deliverables, communicate clearly, bring structure to day-to-day technical work, and contribute to timely team outcomes.
How You'll Create Impact
Principal Duties and Responsibilities:
Design, build, test, and maintain SQL-based data pipelines, transformations, and data models in Snowflake and other approved cloud data environments.
Develop Python-based data processing, automation, and integration workflows.
Ingest and process structured and semi-structured data from databases, APIs, files, SaaS applications, and cloud storage.
Implement ETL/ELT processes using SQL, Python, dbt or equivalent transformation frameworks, Snowflake capabilities, and orchestration tools (such as Dagster or similar).
Participate actively in Agile ceremonies and deliver assigned sprint commitments with quality and urgency.
Use version control and established CI/CD practices to develop, review, test, and deploy data solutions.
Apply data-quality, data-governance, security, and privacy practices to the design and operation of data products.
Troubleshoot data-pipeline issues, investigate root causes, and make performance and reliability improvements.
Maintain clear technical documentation, monitoring, alerting, and operational procedures for supported pipelines.
Collaborate with cross-functional partners to clarify requirements, communicate progress and risks, and deliver fit-for-purpose technical solutions.
What Makes You Stand Out
Technologies required for this role:
SQL and data modeling; Snowflake or a comparable cloud data warehouse preferred.
Python for data pipelines, automation, and integration development.
dbt or comparable SQL-based transformation frameworks.
Airflow, Dagster, or similar orchestration tools.
Git-based version control and CI/CD tools such as GitHub
ETL/ELT and ingestion tools such as Fivetran, Airbyte, Apache NiFi, or comparable technologies.
Cloud services and storage in Azure, AWS, GCP, or equivalent environments.
Expected Areas of Competence:
Strong SQL and Python capabilities, with an ability to apply them to practical data-engineering problems.
Ability to work effectively with cross-functional and Agile teams.
Strong analytical, problem-solving, organizational, and critical-thinking skills.
Clear written and verbal communication skills, including an ability to explain technical information to non-technical partners.
Ability to organize ambiguous work, identify dependencies, and move assigned technical deliverables forward.
Attention to detail in data validation, documentation, monitoring, and operational readiness.
Demonstrated ownership, adaptability, and willingness to learn and apply new technologies.
Ability to collaborate across teams and geographies.
Your Background
Bachelor's degree in Computer Science, Information Technology, Data Engineering, Analytics, or a related technical field required; equivalent combination of education and relevant experience may be considered.
5+ years of experience designing and developing SQL-based pipelines, data transformations, and data models required.
Hands-on experience with Python for ETL/ELT development and automation.
Experience with Snowflake or another cloud data warehouse, and familiarity with cloud data-engineering practices required.
Experience with orchestration platforms such as Airflow, Dagster preferred.
Familiarity with APIs, common data formats(XML, Parquet, JSON), cloud storage, data governance, and security practices preferred.
Experience in Agile delivery environments required; exposure to CDC, streaming ingestion, or AI/ML workflows is a plus.
Communication skills: High proficiency in English (verbal and written)
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