Odaseva

Big Data Engineer

Paris

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type Full time
Employment type in the employer’s posting: Full time
From the employer’s posting

Apply at Odaseva

What you’ll work on

Full posting

Design, build and monitor Spark data pipelines for large-volume data lake ingestion
Optimize performance, reliability and costs of big data pipelines and infrastructure
Work with Databricks, Snowflake or Redshift alongside AWS services for data engineering
Manage and optimize Delta Lake or Apache Iceberg tables
Apply security, governance and compliance practices across storage, compute and sharing layers

From the employer’s posting

Design, build, handle, and monitor Spark data pipelines for large-volume data lake ingestion

Continuously optimize the performance, reliability, and costs of big data pipelines and infrastructure.

Deeply leverage Databricks, Snowflake, or AWS Redshift, alongside cloud provider solutions (AWS S3, Glue/Athena, Lambda)

Demonstrate strong expertise in managing, creating, and optimizing Delta Lake or Apache Iceberg tables.

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

What you’ll bring

All qualifications

Core experience

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.
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.

Qualification wording

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.

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.

Education & alternatives

- 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.

Tools in this posting

Java
Python
SQL
AWS
Databricks
Delta Lake
Iceberg
Redshift
S3
Snowflake
Spark

Source

- 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.

- 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.

- 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).

- 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.

- 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.

Posting record

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Status in our records: Live
First seen by us: Sep 27, 2026 · 34 days ago
Recorded sightings: 23
Last seen by us: Nov 1, 2026
Employer says posted: Sep 23, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

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Job description

View original posting ↗

Odaseva provides enterprise data management and security solutions that help organizations exercise sovereignty over their critical data.

Companies representing 10% of global market capitalization trust Odaseva, including Schneider Electric, Volkswagen and Robert Half.

Its Excalibur Data Platform enables customers to protect, secure, move, and use data and code independently of application vendors.

The platform supports complex and high-volume use cases, including backup, archiving, zero-copy, BCDR & High Availability, data federation, and semantic AI orchestration.

Customer-controlled end-to-end encryption raises the bar for enterprise security.

With 11 patents, Odaseva manages 50 trillion records and supports 100 million users.

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.

Employment type

Full time

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay: No pay amount identified in the saved description.
Location & working pattern: Paris
- Based in Paris (75002), France. - Hybrid: 3 days in the office / 2 days remote work. - Full time permanent contract position.
Work authorization: No clear work-authorization passage found. Eligibility is unconfirmed.

Open source at jobs.lever.co ↗Read the full description ↓

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