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Toptal, a global fully remote workforce platform with $200M+ in annual revenue, is seeking a Data Analytics Engineer to build and maintain the data foundation for world-class analytics and business decision-making.
You will own and maintain the data transformation layer, serving as the product owner for Toptal's data warehouse. Your responsibilities include designing and shipping SQL models that transform raw data into usable products for organizational stakeholders; proactively working with Data Engineers to ensure new data sources are modeled and published; monitoring the warehouse to identify opportunities for improvement in data accuracy, quality, coverage, and usability; turning ambiguous business asks into modeled data through requirements gathering; implementing measures to improve data quality and governance across dozens of production databases; owning the data dictionary with comprehensive documentation; diagnosing and resolving data incidents; reviewing teammates' code; and enabling end users to leverage data effectively.
You will work closely with Business Analysts, Data Engineers, Data Scientists, and business process owners to empower data-driven decision-making. The role emphasizes both technical excellence and business acumen—you must understand Toptal's operations deeply and make independent judgments about what data matters for business objectives.
This role is AI-heavy. Toptal has instrumented its codebase for agentic development, and you will work with LLMs and agentic coding tools daily. However, the role also requires the human judgment machines cannot provide: understanding business metrics deeply and deciding what drives company objectives forward.
Onboarding timeline: Week 1 focuses on integration and environment setup (GCP, BigQuery, Dataform, agentic tooling). Month 1 emphasizes understanding data operations, ETL processes, source systems, and beginning independent Dataform work. By month 3, you will have mastered core data elements, standardized definitions, and become a first responder for data incidents. By month 6, you will contribute architectural ideas. By year 1, you will be the trusted authority on data accuracy and warehouse usability.
Qualifications and Requirements:
- Bachelor's degree required, preferably in Engineering or related technical field
- 4+ years of experience as an Analytics Engineer, Data Engineer, or Data Analyst, with personally shipped production data models
- Expert-level SQL skills and working knowledge of Python
- Fluency with LLMs and agentic coding tools (Claude Code, Cursor, or equivalent) as part of daily practice; understanding of their reliability, limitations, and output verification
- Experience with cloud data warehousing (BigQuery or Snowflake)
- Strong understanding of modern transformation frameworks (dbt, Dataform, SQLMesh, or similar)—dependency graphs, refs, incremental strategies, tests, environment promotion
- Git fluency: branching, pull requests, code review, conflict resolution, production environment management
- Strong familiarity with orchestration tools (Airflow, Cloud Composer, Dagster, Prefect); ability to read DAGs, understand schedules and dependencies, troubleshoot failures
- Process discipline: Jira, ticket hygiene, code review etiquette, and ability to avoid shipping unverified LLM-generated code
- Experience in exploratory/raw data analysis, data modeling, and data governance (quality, accuracy, coverage, security)
- Ability to translate business logic and objectives into SQL and link analytics to business strategy and operations
- Familiarity with BI tools (Tableau, Power BI, etc.)
- Detail-oriented, methodical, thorough, and collaborative
- Outstanding written and verbal communication; ability to explain complex issues simply
- World-class individual contributor mindset; ownership of quality, accuracy, and timeliness
- No visa sponsorship offered; resumes and communication must be in English
