Big Data Engineer
Posted 23 Sep 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 7+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
Field of studyApplied Mathematics, Computer Science, Data Science
Study levelBachelor's or higher
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
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Data Pipeline Development & Operations: Design, build, handle, and monitor Spark data pipelines for large-volume data lake ingestion (handling hundreds of TBs).
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Platform & Cost Optimization: Continuously optimize the performance, reliability, and costs of big data pipelines and infrastructure.
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Databricks, Snowflake & Cloud Engineering: Deeply leverage Databricks, Snowflake, or AWS Redshift, alongside cloud provider solutions (AWS S3, Glue/Athena, Lambda) for scalable data engineering.
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Lakehouse Architecture: Demonstrate strong expertise in managing, creating, and optimizing Delta Lake or Apache Iceberg tables.
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Secure Data Management: Apply security-by-design, data governance, and compliance best practices across storage, compute, and sharing layers.
Required Qualifications
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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.
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Hands-on engineer background with proven track record handling large volumes of data (at least hundreds of TB).
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Deep knowledge of at least one data lake platform among Databricks, Snowflake, or AWS Redshift.
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Strong expertise in Apache Spark (creation, handling, and monitoring of pipelines) and lakehouse table formats (Delta Lake or Apache Iceberg).
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Strong Python or Java and SQL skills, cloud infrastructure (AWS S3, Athena, Glue), and IaC/observability practices.