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Toters is an on-demand e-commerce and delivery platform operating across the Middle East, enabling customers to get anything in their city with high convenience. The company operates a 3-sided marketplace connecting customers, merchants, and couriers, with technology at the core of its operations.
As a Senior Analytics Engineer, you will serve as the critical bridge between Data Engineering and Data Analytics. In this high-volume transactional environment, you will own the data warehouse architecture, build robust data models, and design ELT pipelines that empower analysts, data scientists, and business leaders to make high-impact decisions.
Key responsibilities include:
• Advanced Data Modeling: Design, build, and maintain highly scalable and modular data models using dbt (data build tool) within cloud data warehouses (BigQuery, Snowflake, or Databricks), creating a reliable single source of truth from raw data.
• Pipeline Optimization: Architect and optimize ELT pipelines to integrate complex, high-volume datasets across the marketplace (app clickstreams, operational logistics, merchant catalogs, and financial transactions).
• Engineering Best Practices: Champion software engineering practices within the data team, including version control (Git), CI/CD pipelines, code reviews, and DRY code principles.
• Data Quality & Governance: Implement automated testing, alerting, and anomaly detection to guarantee data integrity and build trust with downstream stakeholders.
• Self-Serve Enablement: Design intuitive semantic layers and well-documented data marts that empower Product Analysts and Business operators to independently explore data without writing complex SQL.
• Performance & Cost Optimization: Audit and optimize legacy queries, streamline warehouse compute resources, and ensure BI tools (Tableau) run with minimal latency.
• Mentorship: Elevate the technical baseline of the entire Data Analytics team by teaching advanced SQL, dbt modeling techniques, and performance-tuning strategies.
Requirements:
• 4+ years of experience in Analytics Engineering, Data Engineering, or a highly technical Data Analytics role, ideally within a high-growth tech company, delivery platform, or multi-sided marketplace.
• Unmatched proficiency in writing complex, highly performant SQL.
• Extensive, hands-on experience building production-grade environments in dbt, including Jinja, macros, and incremental logic.
• Deep conceptual and practical understanding of modern columnar data warehouses (Snowflake, BigQuery, or Databricks) and architecture best practices.
• Fluency in Git workflows, command-line interfaces, and setting up CI/CD workflows for data deployments.
• Strong proficiency in Python for API integrations, custom transformations, or pipeline scripting.
• Demonstrated ability to translate complex operational logic (e.g., courier dispatch algorithms, funnel attribution, financial reconciliation) into elegant data architecture.
Nice to Have:
• Hands-on experience with modern data orchestration tools (Apache Airflow, Dagster, or Prefect).
• Experience managing event-tracking pipelines (Snowplow, Segment, Amplitude).
• Previous experience managing the semantic layer in BI platforms like Tableau.
