AI Data Quality SLA Enforcement Engineer

Velmox Data

Remote · $80k – $100k / year · Contract · Posted 4 months ago

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About the role

Data SLAs are easy to define and hard to enforce. You say the dataset will be refreshed by 8 AM daily with 99.5% row completeness. But who checks? Who gets paged when it's 8:17 AM and completeness is at 94%? Who knows which downstream ML pipelines are blocked, and in what order to fix them? We build the SLA enforcement layer for enterprise data platforms. Your role is to extend our enforcement engine to cover a new class of SLA: ML feature freshness guarantees. When a model expects feature X to be updated every 4 hours, our system needs to detect when that feature is stale, assess impact on model predictions, and route the alert to the right team with enough context to act in under 10 minutes. This is a 9-month contract to build one well-defined feature set. The spec is detailed, the team is experienced, and the deployment target is clear.

Fully remote.

Responsibilities

Build ML feature freshness SLA monitoring and enforcement pipelines
Implement stale feature detection with downstream model impact assessment
Design alert routing logic that reaches the right team with full context
Create SLA compliance dashboards for data and ML platform owners
Test enforcement logic across Airflow, dbt, and Spark pipeline environments
Document feature freshness SLA schema for client onboarding

Requirements

3+ years in data engineering with pipeline monitoring or observability experience
Strong SQL and Python skills for pipeline analysis
Familiarity with ML feature stores or ML pipeline concepts
Experience with alerting systems and on-call tooling
Ability to deliver a scoped feature set independently with minimal supervision