Senior Data Analyst - AI (Gen AI & Recommendation Systems)
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Key skills for this role
About the Role
Salla seeks a Senior Data Analyst/Analytics Engineer to own data and analytics for Gen AI and Recommendation Systems. You will build pipelines, define metrics, and engineer features for ML models, partnering with Data Scientists and Engineers.
Key Skills for This Role
Responsibilities
- Build and maintain scalable, fault tolerant batch and streaming pipelines for analytical and ML use cases
- Define key metrics for each product and build centralized reporting around them
- Design and own multi layer data models (staging to feature ready marts) that stay consistent and performant across ML models, dashboards, and APIs
- Engineer data flows that populate and update the ML Feature Store with low latency for recommendation models
- Build pipelines and metrics frameworks for A/B testing, including experiment schemas, assignment logging, and reliable metric computation
- Own ClickHouse as 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
- Build and manage workflows with modern orchestration tools (e.g. Mage AI, Airflow, Prefect)
- Define and interpret offline and online ranking metrics, and engineer features needed by models
- 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)
- Hands on building pipelines with modern orchestration (Mage AI, Airflow, Prefect)
- 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
Full Job Posting
Role Overview
- 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 centralized reporting and build pipelines and data models, defining the right metrics for each product.
- For Recommendation Systems, you'll bring enough ML understanding to engineer features and evaluation metrics, partnering with Data Scientists, ML Engineers, Product, and Backend teams.
Key Responsibilities
- Pipeline Architecture & Development: Build and maintain scalable, fault tolerant batch and streaming pipelines for analytical and ML use cases.
- Centralized Reporting & Metrics: Define key metrics for each product and build rock solid centralized reporting around them.
- Data Modeling: Design and own multi layer data models (staging to feature ready marts) that stay consistent and performant across ML models, dashboards, and APIs.
- Feature Store & ML Data Flows: Engineer data flows that populate and update our ML Feature Store with low latency recommendation models need.
- Experimentation & A/B Testing: Build pipelines and metrics frameworks behind A/B testing for statistically sound results.
- ClickHouse Mastery: Own ClickHouse as domain expert — schema design, performance tuning, and fast queries.
- Streaming & CDC: Implement Change Data Capture (CDC) and event driven flows (e.g. Apache Kafka) to keep data fresh.
- Orchestration & Automation: Build and manage workflows with modern orchestration tools (e.g. Mage AI, Airflow, Prefect).
- ML Aware Support: Define and interpret offline and online ranking metrics, and engineer features models need.
- Cross Functional Collaboration: Partner with Data Scientists, ML Engineers, Product, and Backend to turn data requirements into production pipelines.
Experience
- 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.
- Hands on building pipelines with modern orchestration (Mage AI, Airflow, Prefect).
- 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 (e.g. Feast, Hopsworks), and how to engineer features at scale.
Nice to Have
- DBT for modeling and transformation.
- Building or integrating A/B platforms (e.g. Statsig, Optimizely, GrowthBook, or custom).
- Apache Kafka and CDC tools (e.g. Debezium, Maxwell).
- Graph Databases (e.g. Dgraph, Neo4j, Amazon Neptune) and structuring data for them.
- JavaScript or Go.
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