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Medal · 1 week ago

Data Infrastructure Engineer

New York, NY

Full-time

Onsite

Senior Level

$170K/yr - $275K/yr

5+ years exp

Medal is a leading platform for gaming moments, focusing on building intelligent systems that understand gameplay. The role involves owning and developing scalable, real-time data infrastructure to support video ingestion and machine learning research.

GamingOnline GamesVideo GamesVideo Streaming

Responsibilities

Architect and operate petabyte-scale ingestion pipelines

Design automated QA guard-rails (schema validation, anomaly detection, deduplication)

Build high-performance ETL and feature-extraction jobs to process and index hundreds of millions of clips into columnar/video-native formats

Own the end-to-end data ingestion stack (desktop, web & mobile clients)

Establish real-time monitoring, lineage, and “five-nines” SLAs, driving continuous improvement across storage, compute, and network layers

Partner with research and product to curate high-signal data slices, data-health metrics, and accelerate model experimentation

Champion security, privacy, and governance: implement robust RBAC, audit trails, and compliant retention policies for sensitive gameplay footage and user inputs

Mentor and uplevel engineers (including internal Medal platform talent), fostering a culture of craftsmanship, documentation, and ruthless focus on data excellence

Qualification

ETL/ELT pipelinesDistributed systemsStreaming data architecturesCloud infrastructurePythonJavaGoData QA methodologiesAnomaly detectionTeam developmentUrgencyMentorshipOwnership

Required

5+ years of experience in data engineering, backend systems, or related roles. Experience with video data or ML infrastructure is a plus

Deep knowledge of ETL/ELT pipelines, distributed systems, and streaming data architectures (e.g., Kafka, Spark, Flink, etc.)

Strong proficiency with Python, Java, Go, or similar languages used in data-intensive environments

Experience with cloud infrastructure (e.g., AWS, GCP), provisioning tools (e.g. terraform, pulumi) and modern data stack tools (e.g., dbt, Airflow, Parquet, Arrow)

Track record of designing systems with extreme scale and performance requirements

Experience consolidating data from diverse sources into unified data models

Deep understanding of data QA methodologies, anomaly detection, and automated testing in production systems

Passion for mentorship and team development; able to upskill engineers and advocate for engineering excellence

A bias toward ownership, urgency, and a desire to build systems that just work, even at scale