Senior Data Operations Lead

We are hiring a Senior Data Operations Lead to be the business owner of the paid marketing data layer for Credible's Israel-based paid marketing team.. This is a senior individual-contributor role at the center of how we measure, optimize, and scale paid acquisition across Google, Bing, OpenAI, Meta, and new platforms as we add them.

Our bidding runs on the conversion data we send to the ad platforms. When that data is late, incomplete, or wrong, performance drops and budget goes to the wrong places. You make sure what the platforms see matches our real numbers, that every new platform, vertical, and campaign is measured correctly from day one, and that our data foundation makes this easier with every launch, not harder.

You will report to the VP Performance Marketing and work day to day with our BI Engineering and Analytics teams. You own the requirements, definitions, standards, and priorities; BI Engineering owns the technical architecture and implementation, and you work closely with them on the design. This is a high-ownership role with direct impact on revenue and margin.

• Paid data foundation: define the business requirements and standards for a unified paid marketing data layer: shared join keys (click IDs, UUIDs), consistent vertical, lender, and campaign fields, naming and URL conventions, and one conversion event framework. Partner with BI Engineering on the design so tracking issues and leakage are easy to detect, reporting works across verticals and platforms, and new verticals plug into a reusable upload mechanism instead of a custom build
• Launch readiness: own the data checklist for every new platform, vertical, and campaign launch: conversion upload definitions, tracking (UTMs, click IDs), revenue mapping, and an end-to-end test before any spend goes live
• Conversion uploads: define what we send to each platform (events, values, cadence) and assess the impact on the data backend and on bidding before any change ships
• Reconciliation, alerting & leakage: own platform-vs-Looker variance and attribution leakage; define thresholds and alerts so discrepancies are caught by systems, not by someone noticing a dashboard looks off
• Data roadmap: own the paid marketing data roadmap: prioritize infrastructure changes, fixes, and reporting needs, write clear specs for BI Engineering and Analytics, and drive them through delivery
• Investigation: dig into data issues independently with SQL and Looker, find the root cause, and put fixes in place that stop the problem from recurring
• Platform onboarding: lead the data side of onboarding new ad platforms and partners, from API conversion uploads to cost ingestion and reporting
• Automation: replace manual checks, uploads, and reports with automated workflows, including AI/LLM tools where they help
• Documentation: maintain the source of truth for conversion definitions, upload logic, and tracking standards across verticals

• Every active vertical and platform has automated conversion uploads with a documented definition
• New verticals launch on a standard data and upload template, not a custom build
• Platform-vs-Looker variance under 5%, with alerts when it drifts
• No launch goes live without passing the data readiness checklist

• Data operations depth: 5+ years in data operations, marketing data, or a similar role, ideally in performance marketing, lead-gen, comparison, or adtech
• Ad platform data (required): hands-on experience with offline/API conversion uploads, click IDs (gclid, msclkid, etc.), UTM and tracking setups across Google Ads, Microsoft Ads, or similar
• Data structure: experience defining data requirements, how datasets connect, and source-of-truth logic, working closely with data engineering teams
• Technical skills: strong SQL; comfortable investigating a data warehouse independently; experience with Looker, Tableau, or a similar BI tool
• Requirements & delivery: proven experience writing requirements for data or engineering teams and driving them to delivery (Jira or similar)
• QA mindset: experience owning QA and validation methodology for data processes
• Ownership mindset: anticipates problems before launch, doesn't wait to be told something is broken, and follows through until it's fixed
• Communication: clear written English; able to explain data issues and trade-offs to marketers, engineers, and a US-based leadership team; comfortable pushing back on priorities with data
• Education: Bachelor's degree preferred

• Experience with APIs, reverse ETL, data pipelines, or identity resolution
• Hands-on experience with dbt or similar tools, including making small, reviewed changes to data models
• Python
• Experience automating QA or monitoring with LLMs or AI tools
• Background at a financial services or comparison marketplace company
• Experience working with US teams remotely