Data Engineering Analyst

Job Duties & Responsibilities

• Design & Build Data Systems: Architect and implement scalable data pipelines, lakehouse/lake/warehouse environments, APIs, and orchestration workflows to support analytics, AI/ML, and business intelligence.
• Enable AI & ML at Scale: Partner with Data Science and AI teams to productionize ML models, automate workflows, and enable AI orchestration frameworks (e.g., MLflow, Airflow, Databricks workflows).
• Technical Leadership: Act as a hands-on subject matter expert in Databricks, Python, Spark, and related technologiesdriving adoption of best practices and mentoring other engineers.
• Optimize Performance: Ensure data pipelines and platforms are highly available, observable, and performant at scale through monitoring, automation, and continuous improvement.
• Ensure Compliance & Security: Build solutions that adhere to data governance, privacy, and regulatory frameworks (HIPAA, SOC 2, GCP, GDPR) within clinical research, life sciences, and healthcare contexts.
• Collaborate Across Functions: Work closely with platform engineering, analytics, product management, and compliance teams to deliver aligned solutions that meet enterprise needs.
• Advance Modern Architectures: Contribute to evolving data platform strategies, including event-driven architectures, data mesh concepts, and lakehouse adoption.

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
• 8+ years of data engineering experience in designing, implementing, and optimizing large-scale data systems.
• Strong proficiency in Python, with production-level experience in building reusable, scalable data pipelines.
• Hands-on expertise with Databricks (Delta Lake, Spark, MLflow), and modern orchestration frameworks (Airflow, Prefect, Dagster, etc.).
• Proven track record of deploying and supporting AI/ML pipelines in production environments.
• Experience with cloud platforms (AWS, Azure, or GCP) for building secure and scalable data solutions.
• Familiarity with regulatory compliance and data governance standards in healthcare or life sciences.

Preferred Qualifications

• Experience with event-driven systems (Kafka, Kinesis) and real-time data architectures.
• Strong background in data modeling, lakehouse/lake/warehouse design, and query optimization.
• Exposure to AI orchestration platforms and generative AI use cases.
• Contributions to open-source projects or published work in data engineering/ML.
• Agile development experience, including CI/CD, automated testing, and DevOps practices.