Senior Analytics Engineer

Role Overview

• Translate ambiguous business questions into structured analytical frameworks
• Develop KPI definitions, metric layers, and standardized reporting logic across teams
• Partner with Product and Growth teams to define success metrics and measurement strategies
• Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency
• Present insights and trade-offs clearly to senior stakeholders and leadership
• Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines
• Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference
• Build experimentation dashboards and automated reporting systems
• Partner with Data Science to productionize predictive models and design reusable ML-ready datasets
• Support feature engineering and monitoring for production ML systems
• Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases
• Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics
• Create trusted, well-documented datasets for product, finance, operations, and growth teams
• Improve data quality, observability, lineage, and governance across the analytics ecosystem
• Optimize performance across warehouse and transformation layers
• Drive best practices in analytics engineering across the organization
• Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind
• Mentor analytics engineers and elevate modeling and measurement standards
• Champion a data-driven culture grounded in experimentation and measurable impact

Requirements

• 7+ years of experience in analytics engineering, data engineering, or advanced analytics roles
• Deep expertise in SQL and modern data modeling techniques
• Strong proficiency in Python (or similar) for data transformation, statistical analysis, and ML integration
• Experience working with modern data stack tools (e.g., Airflow, dbt, Snowflake, BigQuery, Redshift, Spark)
• Proven experience building scalable data warehouse environments
• Strong understanding of statistics, hypothesis testing, and causal inference
• Experience designing and operating A/B testing frameworks
• Ability to define metrics, KPIs, and experimentation standards across teams
• Experience supporting production ML workflows or predictive modeling initiatives
• Strong systems thinking with the ability to connect technical decisions to business outcomes
• Experience in restaurant technology, POS systems, or digital ordering platforms
• Exposure to customer lifecycle analytics, marketplace analytics, or growth experimentation
• Familiarity with real-time data pipelines (e.g., Kafka or similar)
• Experience mentoring engineers or leading cross-functional initiatives

Tech Stack

• Airflow
• Amazon Redshift
• BigQuery
• ETL
• Kafka
• Python
• Spark
• SQL
• Vault