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
Bachelors 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.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Analytics - OtherRole: Data Science & Analytics - OtherEmployement Type: Full time