Senior Data Analyst/Analytics Engineer

We're looking for a Senior Data Analyst/Analytics Engineer to own data and analytics across our Gen AI and Recommendation Systems work. It's a hybrid role: you'll own the centralized reporting that turns data into decisions and build the pipelines and data models that feed it — defining the right metrics for each product we ship rather than waiting for others to prepare your data.

Key Responsibilities

• Build and maintain scalable, fault-tolerant batch and streaming pipelines that serve analytical and ML use cases.
• Define the key metrics for each product we ship and build rock-solid centralized reporting around them, surfacing the trends and insights that matter.
• Design and own multi-layer data models (staging to feature-ready marts) that stay consistent and performant across ML models, dashboards, and APIs, handling schema changes cleanly.
• Engineer the data flows that populate and update our ML Feature Store with the availability and low latency recommendation models need.
• Build the pipelines and metrics frameworks behind A/B testing — experiment schemas, assignment logging, and reliable metric computation for statistically sound results.
• Own ClickHouse as the domain expert — schema design, performance tuning, and fast queries for experiment aggregation and feature serving.
• Implement Change Data Capture (CDC) and event-driven flows (e.g. Apache Kafka) to keep data fresh where reporting and recommendations need it.
• Build and manage workflows with modern orchestration tools (e.g. Mage AI, Airflow, Prefect) for reliable delivery and dependency management.
• Define and interpret the right offline and online ranking metrics, and engineer the features the models actually need.
• Partner with Data Scientists, ML Engineers, Product, and Backend to turn data requirements into production pipelines and actionable ML features.

Requirements

• 4+ years as a Data/Analytics Engineer building data systems for analytics and ML.
• Expert Python and advanced SQL.
• Strong BI/visualization skills (e.g. Looker, Tableau) and good intuition for which metrics matter and how to present them.
• Hands-on building pipelines with modern orchestration (Mage AI, Airflow, Prefect) — you build your own data, not just consume it.
• Deep production experience with ClickHouse (or BigQuery, Snowflake, or similar).
• Hands-on multi-layer modeling (raw, staging, marts) using Kimball, Data Vault, or OBT patterns.
• Solid grasp of experimentation frameworks — assignment, holdouts, metric pipelines, variance reduction.
• Good grasp of the ML lifecycle — how models consume data, how Feature Stores work, and how to engineer features at scale, plus enough ranking-metric knowledge to support Recommendation Systems.

Nice to have

• DBT for modeling and transformation.
• Building or integrating A/B platforms.
• Apache Kafka and CDC tools.
• Graph Databases and structuring data for them.
• JavaScript or Go.

Location
Makkah, Saudi Arabia