Senior Analytics Engineer

About Scrambly

Scrambly is a rapidly scaling adtech startup, recognized in prestigious industry rankings like the AppsFlyer Performance Index and Singular ROI Index, all within 3.5 years of operation. The company is experiencing over 250% year-on-year growth across revenue, workforce, products, and technology, while remaining fully bootstrapped and profitable.

Our vision is to create an innovative alternative to traditional app marketplaces, driven by a loyalty-based rewards ecosystem that connects millions of users with leading mobile applications and delivers unparalleled ROI-focused growth for app advertisers.

Role Overview

We are seeking a Senior Analytics Engineer to architect and operate the data platform supporting Scrambly's commercial operations. You'll develop and maintain data pipelines and analytical models essential for managing expenditure, rewards distribution, and ecosystem security—critical decisions backed by data you create.

This position involves both developing and analyzing: sustaining pipelines that transform raw events, attribution data, and advertiser inputs into dependable tables, and leveraging these datasets to evaluate opportunities, define decision thresholds, and guide business strategy. Collaboration will be close with the Head of Data, upcoming Data Scientists, Backend, User Acquisition, and Account Management teams. This is a pioneering role within a fledgling data team; you will enhance existing infrastructure and implement new systems as needed.

Key Responsibilities

• Design, build, and upkeep analytical data pipelines using Dataform on BigQuery, handling the entire lifecycle from source data to end-user consumption, including datasets for new product development.
• Conduct data-driven commercial analyses, crafting actionable insights and tuning operational rules and thresholds for daily decision-making.
• Own and maintain established data pipelines, reverse-engineering unfamiliar workflows, documenting them thoroughly, and ensuring stable production performance.
• Implement rigorous testing, assertions, and alert mechanisms on data models that influence commercial outcomes, proactively identifying and resolving anomalies before stakeholders are impacted.
• Enhance data engineering practices by optimizing warehouse costs, improving query efficiency, minimizing duplication, and standardizing processes such as code reviews and CI/CD pipelines.
• Collaborate extensively with various teams—ranging from the Head of Data to commercial stakeholders—to prioritize initiatives and integrate data products into broader systems.

Candidate Requirements

• Three or more years in analytics engineering, data engineering, BI development, or related roles with responsibility for production pipelines; practical ownership valued over mere tenure.
• Advanced SQL proficiency and adeptness in analytical data modeling, including concepts like layered warehouse architecture and incremental modeling, with resilience handling complex and imperfect business data.
• Strong analytical reasoning to translate open-ended commercial queries into measurable insights and strategic recommendations.
• Significant experience with Google Cloud ecosystem is highly preferred: BigQuery, Dataform, Cloud Functions, Cloud Scheduler, Pub/Sub, and IAM. Candidates with equivalent expertise in AWS or Azure and similar toolsets will be considered.
• Competence in Python for data manipulation, familiarity with Git workflows and code reviews, and a meticulous mindset toward validating data quality rather than assuming correctness.
• Curiosity and resourcefulness to understand and document pre-existing code and datasets thoroughly.
• Self-motivated, proactive approach with confidence to question data anomalies and communicate concerns openly within a small team environment.
• English language skills at B2 level or higher, both written and verbal, to effectively document systems and collaborate internationally.

Preferred Qualifications

• Experience in adtech or performance marketing datasets, including attribution, mobile measurement partner (MMP) data, cohort lifetime value (LTV) or ROAS analysis, and campaign cost-revenue reconciliation.
• Exposure to fraud detection, anomaly identification, or trust and safety data processes.
• Knowledge of event-driven or near-real-time pipeline architectures using Pub/Sub, Eventarc, Kafka, or low-latency data serving platforms such as Redis.
• Understanding of experimentation methodologies including A/B testing design and evaluation, or experience supporting production machine learning workflows such as feature pipeline development and model monitoring.

What We Offer

• A varied role combining pipeline engineering and strategic data analysis with direct business impact.
• Immediate influence on revenue-related decision-making with visible outcomes in weeks.
• Close collaboration with the Head of Data and significant input in shaping the data team's growth trajectory and culture.
• Compensation and seniority tailored to your experience and contribution, beyond the job title.
• Join a profitable, rapidly expanding, bootstrapped company where data investment is driven by tangible success rather than budgetary cycles.

Additional Information

By applying, candidates consent to their personal information being gathered, processed, and stored exclusively to manage and evaluate their application status within the company.