Odaseva

Backend Engineer

• Design, develop, and maintain highly performant, scalable, and secure applications
• Collaborate directly with Product Managers to understand requirements and deliver solutions
• Work closely with the Infrastructure team for optimization and troubleshooting
• Lead by example in writing clean, well-documented, and testable code
• Mentor and guide junior developers

Senior Backend Developer

QA Engineer

• Join the Quality engineering team
• Work on product-related responsibilities
• Report to the manager of the Quality engineering team

Senior QA Engineer

• Architect and guide large-scale data management initiatives — including backup/recovery, archival, anonymization, and high-volume data migrations — for global enterprise customers.
• Serve as the technical thought partner to customer architects and executives, providing guidance on regulated industry requirements (PII, GDPR, HIPAA, DORA) and best practices.
• Provide architectural leadership to customers on designing scalable data workflows and integrations using AWS, Snowflake, or Databricks, ensuring their workflow system data strategies meet enterprise performance, governance, and compliance requirements.
• Apply practical AI/ML techniques to customer use cases such as intelligent classification, anomaly detection, and automated compliance workflows (experience helpful but growth in this area expected and supported).
• Collaborate with Product and Engineering teams to influence roadmap, validate use cases, and accelerate adoption of new data and AI-driven capabilities.

Senior Technical Architect

Data Engineer

• Design, build, and maintain ingestion/processing pipelines at scale using Python/SQL and Spark; operate within a lakehouse stack (Apache Iceberg or Delta Lake).
• Implement and optimize workflows on Databricks (Jobs, Workflows, Delta) and/or Snowflake (Warehouses, Tasks, Streams) and/or native cloud provider solutions (AWS / AZURE).
• Improve performance, reliability, and cost on AWS (S3, Redshift, Athena/Glue, Lambda), with strong observability and IaC practices.
• Apply security-by-design, data governance, and compliance best practices across storage, compute, and sharing layers.
• Partner with Product team and R&D team to prepare data for initial AI/ML use cases (feature pipelines, data quality, lineage).
• Enable secure, efficient data access for customers via connectors, APIs, and lakehouse sharing patterns (e.g., Delta Sharing, Snowflake data sharing).