Key Details
• Experience Level: Senior (5 to 8 years)
• Job Type: Full Time
• Visa Sponsorship: Unknown
• Industries: Biotechnology, Scientific Research, Healthcare
Responsibilities
Architect, build, and optimize data solutions to support Thermo Fisher Scientific’s digital transformation strategy
Build connections and workflows within cloud-based systems
Build, develop, and deploy scalable data pipelines and ETL/ELT processes using Databricks
Engineer robust data solutions to integrate enterprise data sources, including ERP, CRM, laboratory, and manufacturing systems
Develop reusable frameworks and templates to accelerate data delivery and ensure consistency across domains
Implement and maintain high-performance data connections across Databricks, Snowflake, and Iceberg environments
Author and optimize complex SQL queries, transformations, and data models for analytics and reporting use cases
Support data Lakehouse and data mesh initiatives to enable seamless access to trusted data across the organization
Apply data governance, lineage, and security controls using Unity Catalog, Delta Live Tables, and related technologies
Partner with compliance and cybersecurity teams to uphold data privacy, GxP, and regulatory standards
Establish monitoring, auditing, and optimization processes for ongoing data quality assurance
Collaborate with data scientists, architects, and business partners to build and implement end-to-end data solutions
Serve as a technical mentor and leader with vision within the CRG data engineering community
Contribute to critical initiatives for digital platform modernization and advanced analytics enablement
Requirements
Bachelor's degree or equivalent (combination of appropriate education, training, and/or directly related experience may be considered)
Minimum of 8 years professional experience in data engineering or data platform development
Minimum of 5 years of hands-on experience with Databricks and Apache Spark in production environments
Demonstrated expertise with Snowflake and Apache Iceberg
Strong proficiency in SQL and experience optimizing queries on large, distributed datasets
Proven experience with cloud-based data platforms (Azure preferred; AWS or GCP acceptable)
Strong understanding of data modeling, ETL/ELT pipelines, and data governance practices
Experience implementing Unity Catalog or CI/CD pipelines for data workflows (preferred)
Skills
• Databricks
• Apache Spark
• Snowflake
• Iceberg
• SQL
• ETL
• ELT
• Data Pipelines
• Cloud-Native Data Architectures
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