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 across Gen AI and Recommendation Systems. The role involves building pipelines, centralized reporting, data modeling, and supporting ML feature stores and experimentation frameworks.
Key Skills for This Role
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
- Design and own multi layer data models (staging to feature ready marts) that stay consistent and performant across ML models, dashboards, and APIs
- 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
- 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
- Build and manage workflows with modern orchestration tools (e.g. Mage AI, Airflow, Prefect) for reliable delivery
- 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
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 the centralized reporting that turns data into decisions and build the pipelines and data models that feed it.
- For Recommendation Systems, you'll bring enough ML understanding to engineer the right features and evaluation metrics.
Key Responsibilities
- Pipeline Architecture & Development: Build and maintain scalable, fault tolerant batch and streaming pipelines that serve analytical and ML use cases
- Centralized Reporting & Metrics: Define the key metrics for each product we ship 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 the data flows that populate and update our ML Feature Store with the availability and low latency recommendation models need
- Experimentation & A/B Testing: Build the pipelines and metrics frameworks behind A/B testing
- ClickHouse Mastery: Own ClickHouse as the domain expert
- 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
- ML Aware Support: Define and interpret the right offline and online ranking metrics
- Cross Functional Collaboration: Partner with Data Scientists, ML Engineers, Product, and Backend
Requirements
- Experience: 4+ years as a Data/Analytics Engineer building data systems for analytics and ML
- Programming: Expert Python and advanced SQL
- BI & Visualization: Strong BI/visualization skills (e.g. Looker, Tableau)
- Pipelines & Orchestration: Hands on building pipelines with modern orchestration (Mage AI, Airflow, Prefect)
- Data Warehouse / ClickHouse: Deep production experience with ClickHouse (or BigQuery, Snowflake, or similar)
- Data Modeling: Hands on multi layer modeling (raw, staging, marts) using Kimball, Data Vault, or OBT patterns
- Experimentation & A/B Testing: Solid grasp of experimentation frameworks
- ML Exposure: Good grasp of the ML lifecycle
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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