Design, build, and operate scalable, production-grade data pipelines and curated datasets powering ads optimization and ML systems.
Own end-to-end offline data flows from raw event ingestion to feature-ready datasets ensuring correctness, reproducibility, and SLA compliance.
Develop and maintain large-scale batch and streaming workflows (Python / Java / SQL) with strong focus on performance, cost-efficiency, and reliability.
Contribute to our Feature Store platform, including collaboration with the high-throughput online serving layer (Go-based services).
Translate complex product and monetization logic into durable, extensible data models serving analytics and machine learning use cases.
Improve observability, validation frameworks, and data quality standards across pipelines.
Drive architectural decisions and engineering best practices within the Feature Platform team.

Strong Data Modeling & SQL Expertise: deep experience designing scalable, well-structured data models. Strong understanding of partitioning, schema evolution, and performance optimization.
Production-Grade Pipeline Experience: hands-on experience building and operating large-scale ETL/ELT systems using Python, Java, SQL, or similar technologies in distributed environments.
Distributed Processing: experience with frameworks such as Spark or Flink. Strong understanding of parallel computation and performance tuning.
Systems Awareness: understanding of how offline data systems integrate with online serving layers (e.g., feature stores, APIs, low-latency systems).
Experience with Go is a plus, particularly for collaboration on high-throughput feature serving services.
Modern Cloud Infrastructure: experience working in cloud-native environments, containerized systems, Kubernetes, and orchestration tools.
Ownership & Reliability Mindset: strong focus on data correctness, observability, and long-term maintainability. Ability to independently drive complex initiatives.

Senior Data Engineer

Key skills