Senior Python Engineer Data Platform · Remote (worldwide) · Full time View role →

Stat Sniper Inc.; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Stat Sniper Inc. is seeking a senior data engineer to take models from research to production.

You will build production data pipelines that land raw feeds in S3 and modeled tables in Postgres, and ship model-serving containers to Cloud Run and ECS Fargate with predictable latency during slate-day load. You will serve precomputed features and predictions from DynamoDB, own the grading jobs that settle bets against official results, and raise the bar for the data codebase with typing, tests, and

Qualifications

• Senior-level Python with real production ownership.
• Experience with orchestration (Airflow, Prefect, or similar) and with idempotent, retryable jobs.
• Strong SQL and comfort designing analytical schemas in Postgres.
• Practical AWS and GCP experience — S3, DynamoDB, and Fargate on one side, Cloud Run, Cloud SQL, and Pub/Sub on the other.
• A track record of taking someone else’s model and making it run every day without drama.

Responsibilities

• Build production data pipelines that land raw feeds in S3 and modeled tables in Postgres on a schedule.
• Ship model-serving containers to Cloud Run and ECS Fargate with predictable latency under slate-day load.
• Serve precomputed features and predictions from DynamoDB where the app needs single-digit-millisecond reads.
• Own the grading jobs that settle every logged bet against the official result, and the writes that surface the result in the app.
• Set the engineering standard for the data codebase: typing, tests, and reproducible runs.

Skills

Python

Production ownership

Airflow

Prefect

SQL

PostgreSQL

Cloud (AWS/GCP)

S3

DynamoDB

Cloud Run

ECS Fargate

Pub/Sub

Tools

Airflow

Prefect

PostgreSQL

DynamoDB

Cloud Run

ECS Fargate

Cloud SQL

Pub/Sub

Job description

Take the models from research to production and keep them there. You'll build the pipelines that assemble training data, the services that serve predictions during a live slate, and the grading jobs that score every pick against the final box score — then make all of it reliable enough that nobody has to babysit it on a Sunday.

What you'll do

• Build production data pipelines that land raw feeds in S3 and modeled tables in Postgres on a schedule.
• Ship model-serving containers to Cloud Run and ECS Fargate with predictable latency under slate-day load.
• Serve precomputed features and predictions from DynamoDB where the app needs single-digit-millisecond reads.
• Own the grading jobs that settle every logged bet against the official result, and the writes that surface the result in the app.
• Set the engineering standard for the data codebase: typing, tests, and reproducible runs.

What we're looking for

• Senior-level Python with real production ownership, not notebook-only experience.
• Experience with orchestration (Airflow, Prefect, or similar) and with idempotent, retryable jobs.
• Strong SQL and comfort designing analytical schemas in Postgres.
• Practical AWS and GCP experience — S3, DynamoDB, and Fargate on one side, Cloud Run, Cloud SQL, and Pub/Sub on the other.
• A track record of taking someone else's model and making it run every day without drama.

Nice to have

• You have been on call for a pipeline that had to be right by kickoff.
• Experience with a feature store, or with the reasons you chose not to use one.
• Comfort pairing with a data scientist and turning a notebook into a service without rewriting it twice.