Senior Data Engineer

Unity Technologies•Washington, DC

About The Position

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. 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. 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. 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.

Requirements

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.

Nice To Haves

Experience with ML infrastructure or feature stores.
Experience working with ads, attribution, or monetization data.
Familiarity with experimentation and metrics pipelines.
Exposure to high-scale backend systems.

Responsibilities

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.