About Toters
Toters is an on-demand e-commerce and delivery platform and operates a service that enables customers to get anything in their city at the highest level of convenience.
At Toters, technology is at the heart of everything we do. We have product teams that are working hard every day to create products that make our customers' lives easier. Our engineers are also continuously creating solutions to make our processes more efficient, all in an effort to get to our customers fast and at the best cost. If you are interested in working in a high growth startup environment, and look to be part of a team that will potentially change the way customers shop in the Middle East, apply now.
The Role
As a Senior Analytics Engineer, you will act as the crucial bridge between Data Engineering and Data Analytics. In a fast-paced, high-volume transactional environment like ours, clean, reliable, and scalable data is everything. You will bring software engineering best practices to our analytics workflows, taking ownership of our data warehouse architecture, building robust data models, and designing the ELT pipelines that empower our analysts, data scientists, and business leaders to make high-impact decisions.
What You’ll Do
- Advanced Data Modeling: Design, build, and maintain highly scalable and modular data models using dbt (data build tool) within our cloud data warehouse (e.g., BigQuery, Snowflake, or Databricks), turning raw data into a reliable "single source of truth."
- Pipeline Optimization: Architect and optimize ELT pipelines to integrate complex, high-volume datasets across our 3-sided marketplace (app clickstreams, operational logistics, merchant catalogs, and financial transactions).
- Engineering Best Practices: Champion and enforce software engineering practices within the data team, including version control (Git), CI/CD pipelines, code reviews, and writing DRY (Don't Repeat Yourself) code.
- Data Quality & Governance: Implement rigorous automated testing, alerting, and anomaly detection to guarantee data integrity and build trust with downstream stakeholders.
- Self-Serve Enablement: Design intuitive semantic layers and heavily 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.
What We’re Looking For
- Experience: 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.
- SQL & dbt Mastery: Unmatched proficiency in writing complex, highly performant SQL. Extensive, hands-on experience building production-grade environments in dbt (including Jinja, macros, and incremental logic).
- Cloud Data Warehousing: Deep conceptual and practical understanding of modern columnar data warehouses (Snowflake, BigQuery, or Databricks) and architecture best practices.
- Software Engineering Mindset: Fluent in Git workflows, command-line interfaces, and setting up CI/CD workflows for data deployments.
- Programming Skills: Strong proficiency in Python for API integrations, custom transformations, or pipeline scripting.
- Business Acumen: 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.
