Senior Data Engineer

<p><strong>The opportunity</strong><br>At Unity, we’re building a world-class advertising platform that operates at a massive global scale. The Ads team powers the systems that connect advertisers with creators and players, driving performance, reliability, and growth across our ecosystem.</p> <p>As a Data Engineer on the Feature Platform team, you’ll play a critical role in building the data foundations that power machine learning, experimentation, and optimization across our ads ecosystem. You’ll operate at the core of our ML infrastructure, transforming raw, high-volume event data into reliable, production-grade feature datasets used for bidding, attribution, and ranking systems.</p> <p>You will work at the intersection of business logic, large-scale distributed processing, and platform engineering — translating complex and evolving product requirements into scalable, well-modeled, and reproducible data systems. This role goes beyond analytics enablement; it focuses on building durable, high-quality data infrastructure that supports both offline training workflows and online feature serving.</p> <p>This role is ideal for someone who enjoys owning data architecture end-to-end, collaborating closely with ML, backend, and product teams, and designing systems that prioritize correctness, performance, and long-term maintainability at scale.</p> <p><strong>What you'll be doing</strong></p> <ul> <li>Design, build, and operate scalable, production-grade data pipelines and curated datasets powering ads optimization and ML systems.</li> <li>Own end-to-end offline data flows from raw event ingestion to feature-ready datasets ensuring correctness, reproducibility, and SLA compliance.</li> <li>Develop and maintain large-scale batch and streaming workflows (Python / Java / SQL) with strong focus on performance, cost-efficiency, and reliability.</li> <li>Contribute to our Feature Store platform, including collaboration with the high-throughput online serving layer (Go-based services).</li> <li>Translate complex product and monetization logic into durable, extensible data models serving analytics and machine learning use cases.</li> <li>Improve observability, validation frameworks, and data quality standards across pipelines.</li> <li>Drive architectural decisions and engineering best practices within the Feature Platform team.</li> </ul> <p><strong>What we're looking for</strong></p> <ul> <li>Strong Data Modeling SQL Expertise: deep experience designing scalable, well-structured data models. Strong understanding of partitioning, schema evolution, and performance optimization.</li> <li>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.</li> <li>Distributed Processing: experience with frameworks such as Spark or Flink, in both: streaming, and batch mode. Strong understanding of parallel computation and performance tuning.</li> <li>Systems Awareness: understanding of how offline data systems integrate with online serving layers (e.g., feature stores, APIs, low-latency systems).</li> <li>Experience with Go is a plus, particularly for collaboration on high-throughput feature serving services.</li> <li>Modern Cloud Infrastructure: experience working in cloud-native environments, containerized systems, Kubernetes, and orchestration tools.</li> <li>Ownership Reliability Mindset: strong focus on data correctness, observability, and long-term maintainability. Ability to independently drive complex initiatives.</li> </ul> <p><strong>You might also have</strong></p> <ul> <li>Experience with ML infrastructure or feature stores.</li> <li>Experience working with ads, attribution, or monetization data.</li> <li>Familiarity with experimentation and metrics pipelines.</li> <li>Exposure to high-scale backend systems.</li> </ul> <p><strong>Additional information</strong></p> <ul> <li>Relocation support is not available for t ... (truncated, view full listing at source)