Mirion Technologies is seeking a Senior Data Engineer to architect and lead the design of enterprise data platforms and infrastructure. The role requires a combination of technical expertise and mentorship, focusing on translating complex business requirements into scalable data solutions while driving technical strategy and best practices.
Responsibilities:
- Architect and design enterprise-scale data platforms and pipeline solutions using Spark, Azure Databricks, and related technologies
- Build and optimize data models and dimensional schemas for complex analytics and AI/ML use-cases
- Lead technical design reviews and mentor junior data engineers on best practices and architectural patterns
- Establish data quality, governance, and metadata management frameworks across the platform
- Collaborate with stakeholders to define data requirements and translate them into technical solutions
- Drive optimization initiatives for data pipeline performance, cost, and reliability
- Participate in hiring and team building for the data engineering function
- Contribute to architectural decisions and long-term platform strategy
- Troubleshoot complex data pipeline failures and implement robust monitoring and alerting solutions
Requirements:
- 5+ years experience in data engineering, analytics engineering, or related field
- Expert-level SQL and experience with modern data warehouses (Snowflake, BigQuery, Redshift, etc.)
- Deep experience designing and maintaining large-scale data pipelines using Spark, Airflow, or similar orchestration tools
- Strong proficiency in Python or Scala for complex data processing
- Advanced understanding of data modeling, dimensional design, data warehousing concepts
- Experience with cloud platforms (AWS, GCP, or Azure) at scale
- Proven ability to mentor and guide junior engineers
- Experience architecting data platforms from the ground up
- Experience with Databricks, Delta Lake, and lakehouse architectures
- Expertise in real-time data streaming and event-driven architectures (Kafka, Kinesis)
- Knowledge of data governance, data lineage, and metadata management systems
- Experience with ML infrastructure and feature stores
- Background in regulatory-heavy industries or complex compliance requirements
- Experience with infrastructure-as-code and DataOps practices