Sr Data Architect

Sound engineering judgment, a continuous-improvement mindset, and the ability to deliver reliable pipelines in a fast-paced environment with competing priorities. #LI-AH1 embecta is an Equal Opportunity/Affirmative Action Employer. We do not unlawfully discriminate on the basis of race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, or any other protected status.

About Embecta

• embecta is a global medical device company with a 100-year legacy in insulin delivery and diabetes care.
• Driven by innovation, purpose, and a commitment to improving lives, we are expanding our portfolio of solutions to serve even more patients, healthcare providers, and communities around the world.

Responsibilities

• Design, build, and maintain scalable production ETL/ELT pipelines across Azure, Databricks, and SAP source systems in accordance with enterprise architecture and engineering standards.
• Develop and optimize Databricks notebooks and jobs, Delta Lake tables, and Unity Catalog permissions to support BI reporting and AI/ML workloads.
• Integrate data from SAP S/4HANA, SAP Datasphere, and other enterprise applications into the lakehouse using Azure Data Factory, Microsoft Fabric, or equivalent orchestration tools.
• Implement data-quality checks, monitoring, observability, and alerting so pipeline issues are identified and resolved before affecting downstream reporting or AI/ML consumers.
• Build and tune curated datasets and semantic models that support Power BI reporting, standard KPIs, and single-source-of-truth dashboards.
• Partner with data architects and senior engineers to define and implement enterprise data-integration patterns and reference architectures.
• Write clean, tested, and version-controlled code, and actively participate in code reviews and CI/CD practices for data pipelines.
• Own pipeline reliability by troubleshooting production issues, participating in on-call or escalation rotations as needed, and documenting root causes and corrective actions.
• Collaborate within the Hub & Spoke model with analytics engineers and business teams to understand data needs and promote reusable data-product standards.
• Mentor junior data engineers on coding standards, pipeline design, troubleshooting, and platform conventions.
• Stay current on capabilities across Azure, Microsoft Fabric, Databricks, and the broader data-engineering landscape, and provide practical recommendations to the team.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field; an advanced degree is preferred.
• 8+ years of experience in data engineering, ETL/ELT development, or a related data-platform engineering role.
• Hands-on experience building and operating pipelines in Azure Data Factory and Databricks using Python, PySpark and/or Spark SQL, including Delta Lake table design.
• Experience designing and maintaining enterprise data pipelines in cloud environments, with attention to cost, performance, reliability, and scalability tradeoffs.
• Willingness to work overlapping hours with US and other global stakeholders as needed for cross-time-zone collaboration.
• Strong SQL and Python skills, supported by sound software-engineering practices in version control, testing, code review, and CI/CD.
• Working knowledge of Azure data services, including Azure Data Lake, Azure Data Factory, Azure Databricks, and Microsoft Fabric or comparable cloud services.
• Knowledge of Databricks or modern lakehouse platforms, Delta Lake, and Unity Catalog or similar access-control tooling.
• Experience with Power BI, enterprise analytics, semantic models, dimensional modeling, star schemas, and data-integration patterns.
• Working knowledge of data governance, data quality, monitoring, and observability practices.
• Clear, proactive verbal and written communication, including the ability to raise issues, document technical designs and decisions, and collaborate with architects, analysts, and business stakeholders.
• Ability to operate in a Hub & Spoke or federated data organization, following centrally established standards while adapting to domain-specific needs.

Preferred

• Experience supporting both BI and AI/ML data needs, including curated datasets for Power BI and feature pipelines for model training.
• Preferred experience in manufacturing, supply chain, life sciences, healthcare, or another regulated industry.
• Practical experience with SAP S/4HANA and SAP Datasphere data extraction is preferred.