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Salary: USD 179,000 - 253,500 / annual
Scribd is seeking a senior analytics engineer to own the data foundations for payments, identity, and trust & safety systems. This is a purely technical leadership role with no direct reports, reporting to the Director of Analytics Engineering.
You will be responsible for building canonical data models for payments, billing, and subscriptions in Databricks that work seamlessly across Scribd, Everand, and Fable products. You'll own the KPI and semantic layer for payments and identity exposed through self-serve dashboarding and AI tools. Your work will extend to trust & safety, owning models and metrics for content prevalence and exposure, including evaluation of AI-generated moderation labels.
A core part of your mandate is ensuring data quality and observability so that regional changes, processor routing shifts, or schema migrations are caught within days rather than quarters. You'll drive quality standards, set modeling conventions, influence source-system design, and mentor other analysts and engineers in these domains. You'll also build AI tools on trusted, certified foundations that stay in sync as definitions evolve.
You will work embedded with Product Analytics, Data Science, Product, and Engineering partners. The role emphasizes that metric definitions are engineering artifacts—lineage, observability, and data quality matter as much as the dashboard. You are expected to be equally comfortable building pipelines, defining metrics, and defending numbers in executive rooms. You communicate early and plainly, document methodology, and use AI pragmatically where it creates measurable value.
Scribd operates on a Flex model with occasional in-person attendance required. The company is building an AI-native data organization that turns fragmented data into trusted, production-quality foundations.
REQUIREMENTS:
- 8+ years of experience in analytics engineering, data engineering, business intelligence engineering, or related discipline
- Deep, hands-on payments expertise with direct experience from Stripe, Adyen, Braintree, PayPal, or similar processors
- Practical experience with identity and authentication data
- Depth in governed, canonical data models and semantic layers; proven track record of defining metrics, defending them under scrutiny, and versioning when inputs changed
- Advanced SQL and production experience with Databricks, AWS, Looker, Airbyte, Fivetran, or comparable platforms, including orchestration, transformation frameworks, and automated testing
- Experience making data reliable for AI consumption
- Proven ability to influence senior product and engineering leaders and translate complex payment economics into clear business trade-offs
NICE TO HAVE:
- Advanced Python and experience building AI agents or LLM-based pipelines
- Experience with trust and safety or integrity measurement
- Experience with local payment methods across LATAM, APAC, or EU markets, and with stored-credential and network compliance frameworks
- Experience with on-call rotations and incident response for production data systems
