Senior Data Engineer

Description :

The Digital product engineering team at USP is seeking a Data Engineer with expertise in building robust data pipelines, managing large-scale data infrastructure, and enabling advanced analytics capabilities.

This role is critical to supporting projects that align with our mission to protect patient safety and improve the health of people around the world.

We are looking for a professional who understands the value of clean, accessible, and well-organized data, and who enjoys designing systems that empower data scientists, analysts, and business stakeholders to derive actionable insights.

The ideal candidate will be passionate about data architecture, cloud technologies, and scalable engineering solutions that drive innovation and impact.

How will YOU create impact here at USP?

- Implement scalable data collection, storage, and processing pipelines to support enterprise-wide data needs.

- Implement and maintain data governance frameworks and data quality checks within data pipelines to ensure compliance and reliability.

- Build and optimize data models and data marts to support self-service analytics and reporting tools such as Tableau, Looker, and Power BI.

- Partner with data scientists to operationalize models by integrating them into production-grade pipelines, ensuring scalability, performance, and maintainability.

- Collaborate with cross-functional stakeholders (business, product, analytics, and engineering teams) to translate business requirements into scalable data solutions and prioritize data initiatives.

- Collaborate with platform, DevOps, and security teams to ensure secure, cost-effective, and scalable data infrastructure.

Who USP is Looking For?

The successful candidate will have a demonstrated understanding of our mission, commitment to excellence through inclusive and equitable behaviors and practices, ability to quickly build credibility with stakeholders, along with the following competencies and experience :

Education :

Bachelor's degree in relevant field (e.g. Engineering, Analytics or Data Science, Computer Science, Statistics) or equivalent experience.

Experience :

- 3+ years of experience in big data technologies such as Python, PySpark, and SQL for processing structured, semi-structured, and unstructured data.

- Hands-on experience with AWS data services including Redshift, S3, Glue, Lambda, EventBridge, Postgres; Exposure to Graph databases such as Neo4j is preferred.

- Experience in building batch, micro-batch, and streaming pipelines (real-time / near real-time) using Lambda/Kappa architectures.

- Hands-on expertise in designing and delivering enterprise-scale data platforms, including data lakehouse, data warehouse, data lake, and data marts.

- Understanding data modeling techniques including - Data Vault 2.0, Dimensional Modeling, Knowledge Graphs is a plus.

- Experience with medallion architecture and metadata-driven data pipeline frameworks.

- Experience in data governance frameworks, including - Data discovery, Data quality, Data security.

- Strong SQL and programming skills for data transformation, modeling, and analysis.

- Hands-on experience in building and maintaining complex ETL/ELT pipelines and managing day-to-day data operations.

- Experience with workflow orchestration tools such as Airflow.

- Knowledge of streaming/event-driven architectures and modern data processing patterns.

- Good understanding of dashboarding and visualization techniques with tools like Tableau, Power BI, or equivalent.

- Experience with Agile (SAFe) methodologies, CI/CD pipelines, and modern deployment practices for data platforms.

Additional Desired Preferences :

- Experience with scientific chemistry nomenclature or prior work experience in life sciences, chemistry, or hard sciences or degree in sciences.

- Experience with pharmaceutical datasets and nomenclature.

- Ability to explain complex technical issues to a non-technical audience.

Skills

Python, SQL, PySpark, AWS, Data Engineering, ETL, Apache Airflow