Data Engineer at Michelin designing and supporting data analytics systems. Involved in data transformation and pipeline quality assurance.
About the role
- Data Solutions Architect designing enterprise-scale Azure and Databricks Lakehouse solutions for clinical trials and life sciences data enabling advanced analytics and compliance.
Responsibilities
- Architect Lakehouse solutions leveraging Azure Data Lake (ADLS Gen2), Databricks, and Delta Lake.
- Design data models (star, snowflake, data vault), ingestion pipelines, and CDC strategies with schema evolution and performance tuning.
- Implement data governance, security, and compliance aligned with GxP, HIPAA, and 21 CFR Part 11.
- Enable data science and ML workflows using MLflow, Feature Store, and curated datasets.
- Collaborate with clinical operations and biometrics teams to deliver business-aligned solutions.
Requirements
- 15+ years in data architecture/engineering; 5+ years with Azure; 3+ years with Databricks.
- Azure Expertise: ADLS Gen2, Data Factory/Fabric pipelines, Synapse/SQL, Event Hubs, Functions, Key Vault, Private Endpoints, VNets.
- Databricks Expertise: Spark (PySpark/SQL), Unity Catalog, Delta Live Tables (DLT), Workflows, MLflow, Feature Store.
- Data Modeling: Star/snowflake, data vault, CDC, schema evolution, performance tuning.
- Programming: PySpark, SQL; bonus: Python (pandas), Scala, dbt.
- Governance & Security: IAM/RBAC/ABAC, row/column-level security, encryption, masking/tokenization, secrets, audit.
- Observability & Reliability: Monitoring, lineage, alerting, CI/CD (GitHub Actions/Azure DevOps), automated testing/validation.
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