Key Responsibilities
Data Pipeline Development & Operations: Design, build, handle, and monitor Spark data pipelines for large-volume data lake ingestion (handling hundreds of TBs).
Platform & Cost Optimization: Continuously optimize the performance, reliability, and costs of big data pipelines and infrastructure.
Databricks, Snowflake & Cloud Engineering: Deeply leverage Databricks, Snowflake, or AWS Redshift, alongside cloud provider solutions (AWS S3, Glue/Athena, Lambda) for scalable data engineering.
Lakehouse Architecture: Demonstrate strong expertise in managing, creating, and optimizing Delta Lake or Apache Iceberg tables.
Secure Data Management: Apply security-by-design, data governance, and compliance best practices across storage, compute, and sharing layers.
Required Qualifications
7+ years of experience in data engineering or backend data platforms, with at least 3 years of hands-on experience building and operating big data pipelines.
Hands-on engineer background with proven track record handling large volumes of data (at least hundreds of TB).
Deep knowledge of at least one data lake platform among Databricks, Snowflake, or AWS Redshift.
Strong expertise in Apache Spark (creation, handling, and monitoring of pipelines) and lakehouse table formats (Delta Lake or Apache Iceberg).
Strong Python or Java and SQL skills, cloud infrastructure (AWS S3, Athena, Glue), and IaC/observability practices.
Solid understanding of cost management principles, data security, governance, and data modeling.
Equivalent engineering school degree or Master’s degree in Computer Science, Data Science, or Applied Mathematics.
Fluent in English and French, with clear communication, ownership mindset, and strong collaborative skills.
Where you'll be
- Based in Paris (75002), France.
- Hybrid: 3 days in the office / 2 days remote work.
- Full time permanent contract position.
Skills Required
- 7+ years of experience in data engineering or backend data platforms
- At least 3 years of hands-on experience building and operating big data pipelines
- Hands-on engineering experience handling large data volumes of at least hundreds of terabytes
- Deep knowledge of at least one data lake platform: Databricks, Snowflake, or AWS Redshift
- Strong expertise in Apache Spark, including pipeline creation, operation, and monitoring
- Strong expertise in Delta Lake or Apache Iceberg table formats
- Strong Python or Java and SQL skills
- Experience with AWS S3, Athena, and Glue
- Experience with infrastructure as code and observability practices
- Understanding of cost management, data security, governance, and data modeling
- Engineering school degree or Master’s degree in Computer Science, Data Science, or Applied Mathematics
- Fluency in English and French
- Clear communication, ownership mindset, and strong collaborative skills
What We Do
At enterprise scale, Salesforce data is different. Data volumes are large. Data models are more sophisticated. Integrations, regulations, and business processes are much more intricate. All this complexity dramatically increases the risks to your data threatening to grind business to a halt. Odaseva is the only data platform built specifically to help the world's largest, most ambitious Salesforce customers keep their data protected, compliant, and agile. With Odaseva, Salesforce architects and platform owners get a powerful set of tools to help solve the problems at the foundation of the Salesforce data value chain. Keep customer data intact and available with comprehensive backup and archiving, apply analytics to prevent disruptions before they happen, use automation to take control of the entire data lifecycle and solve privacy and compliance issues at the root, and easily move data between production and non-production environments, to sandboxes, and to systems outside Salesforce. White Paper: Odaseva Complete Guide to Salesforce Backup and Restore https://www.odaseva.com/complete-guide-to-salesforce-backup-and-restore
