Member of Technical Staff (Data Platform)

Astrocade•Palo Alto, CA

About The Position

Our data infrastructure needs to be built, from scratch. We’re migrating off a custom in-house pipeline that can’t keep up with our growth, and the decisions made now will shape how product, growth, and ML operate for years. As our first Data Engineer, you’ll own the architecture, build the pipelines, and lay the foundation everything else runs on. You’ll sit in engineering, report to the CTO, and work directly with the co-founders. This is a 0→1 build with full ownership.

Requirements

4-7 years of data engineering experience, including at least one 0→1 build or major pipeline rebuild
Can write, profile, and debug SQL at an expert level and explain exactly why a query is slow
Have hands-on AWS data experience: S3, Glue, and the surrounding service landscape
Have migrated or replaced production pipelines without dropping data or breaking downstream consumers
Understand latency tradeoffs - you know when milliseconds matter and when daily batches are fine
Have enough analytical range to serve product and growth teams directly, not just build pipes they can’t use
Are self-directed and comfortable making architectural decisions without a complete spec

Nice To Haves

Experience with high-volume event data from consumer gaming, social, or UGC platforms
Background supporting ML feature pipelines or training data infrastructure
Prior experience as the first or only data engineer at a startup
Familiarity with monetization or ads data infrastructure
Experience with dbt, Airflow, or Dagster

Responsibilities

Build and maintain scalable pipelines for high-volume user and game event data across ingestion, transformation, and serving
Design the data models and warehouse structure that product, growth, and future ML systems depend on
Write and optimize SQL queries - profiling, tuning, and setting the performance standard
Own data quality from day one: monitoring, alerting, and anomaly detection
Build toward a self-serve analytics layer that non-engineers can trust and use independently
Instrument systems deeply: structured logging, performance profiling, latency awareness across the stack