Odaseva6d ago

Data Engineer

ParisFull timemid

Data EngineerData

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Overview

Since 2012 Odaseva has helped global enterprises secure and manage their most valuable asset: data. The Odaseva EnterpriseData Platform secures and manages Salesforce data, ensuring data resilience,

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Data EngineerData

Since 2012 Odaseva has helped global enterprises secure and manage their most valuable asset: data.

The Odaseva EnterpriseData Platform secures and manages Salesforce data, ensuring data resilience, regulatory compliance, and unlocking the value of data. It’s built to solve the complex challenges of large-scale global enterprises.

We’re a fast-growing scale-up with offices in San Francisco, Paris, Sydney, London, Kuala Lumpur, Singapore, and more.

We serve a global customer base including Fortune 500 companies, government organizations, and NGOs supporting more than 100 million Salesforce users worldwide.

At Odaseva, our values, Trust, Service, Commitment, Excellence, Kaizen, and One Team, define the environment we foster for our employees to thrive and succeed.

Join Odaseva’s R&D team and help shape the future of our data platform.

We’re a SaaS company specializing in secure data management around Salesforce environments.

You’ll design, build, and optimize scalable data pipelines and help kick-start our first AI use cases.

Data Pipeline Development: Design, build, and maintain ingestion/processing pipelines at scale using Python/SQL and Spark; operate within a lakehouse stack (Apache Iceberg or Delta Lake).

Databricks & Snowflake Engineering: Implement and optimize workflows on Databricks (Jobs, Workflows, Delta) and/or Snowflake (Warehouses, Tasks, Streams) and/or native cloud provider solutions (AWS / AZURE).

Platform Optimization: Improve performance, reliability, and cost on AWS (S3, Redshift, Athena/Glue, Lambda), with strong observability and IaC practices.

Secure Data Management: Apply security-by-design, data governance, and compliance best practices across storage, compute, and sharing layers.

AI Use Case Enablement: Partner with Product team and R&D team to prepare data for initial AI/ML use cases (feature pipelines, data quality, lineage).

Data Sharing & Integration: Enable secure, efficient data access for customers via connectors, APIs, and lakehouse sharing patterns (e.g., Delta Sharing, Snowflake data sharing).

Equivalent engineering school degree, or a Master's degree in Computer Science, Data Science, or Applied Mathematics.

7–12 years in data engineering or backend data platforms.

Strong Python and SQL; experience with Spark and modern ELT/Orchestration (e.g., dbt, Airflow).

Hands-on with Databricks and/or Snowflake in production.

Experience on AWS (S3, Glue/Athena, Redshift, Lambda) and lakehouse formats (Iceberg or Delta Lake).

Familiarity with data security, governance, and compliance.

Proven experience with data modeling.

Knowledge of cost management principles.

Salesforce data knowledge is a plus, not mandatory.

Foundational AI/ML understanding and motivation to contribute to early use cases.

Fluent in English and French, clear communication, ownership mindset, and collaborative approach.

Excellent interpersonal skills and ability to interact with diverse business stakeholders