Senior Data Engineer

Що робити

Partner with the Principal Engineer, senior technical leaders and platform teams to implement and evolve the architectural direction for metadata-processing systems; independently own the design and delivery of assigned components.
Evaluate and evolve orchestration for enrichment and backfill workloads, either by extending existing ingestion capabilities or integrating with new cloud-native services, while maintaining consistent contracts and lifecycle handling.
Evolve shared frameworks so data scientists can package, test, validate and run production enrichment workloads with minimal data engineering support.
Define and maintain metadata contracts, lineage, validation and quality controls.
Contribute to and apply shared engineering standards, and provide technical guidance through design reviews, documentation and collaboration with data engineering and data science teams.

6+ years of experience in data engineering, backend software engineering or distributed systems, including hands-on ownership of large-scale production data workloads.
Demonstrated experience designing and operating large-scale ingestion or distributed data-processing systems.
Strong Python software engineering and distributed processing experience.
Practical knowledge of high-volume batch and event-driven architectures, including idempotency, replay, backpressure, retries and failure recovery.
Experience enabling data science or ML workloads to move from prototype to production through reusable frameworks and engineering guardrails.

Containerized CPU/GPU batch and ML workloads using managed compute services such as SageMaker, AWS Batch, ECS, Cloud Batch or GKE/EKS.
Event-driven orchestration and messaging using services such as Step Functions, Lambda, EventBridge, Kinesis, Workflows or Pub/Sub.
Snowflake, ClickHouse or Apache Iceberg.
Production-scale image, video or audio processing