Data Analytics Engineer

Toptal prides itself on being a data-driven organization. The primary objective of the Data Analytics Engineer is to help drive business impact and better decision making by laying the data foundation for a world-class analytics function. This role will be critical to fostering trust in our data and confidence in our decisions.

Our Data Analytics Engineers focus on creating a data environment conducive to analytics and business decision-making. You will own and maintain the data transformation layer, requiring expertise in data governance (quality, accuracy, coverage, security), data modeling (structure, relationships, integrity), technical communication (data dictionaries, user training), quality control (code reviews, data validation), raw data analysis, and building AI data systems and pipelines. You will be the product owner for our data warehouse and coordinate closely with Business Analysts and Data Engineers.

This role ensures trustworthy data is available for all downstream data users. Success requires living and breathing SQL daily, critical thinking, problem-solving, and self-starter mentality. This role requires an AI-heavy workflow and skillset, with fluency in using LLMs and agentic coding tools. You must understand business metrics deeply and make decisions aligned with company objectives.

This is a remote position. Visa sponsorship is not offered. Resumes and communication must be in English.

Responsibilities:

The following information describes the general nature and level of work but is not exhaustive.

• Design, write, review, and ship SQL models across repositories transforming raw data into usable data products for stakeholders. Own and maintain the transformation layer.
• Proactively work with Data Engineers to ensure new data sources are added, modeled, and published in the data warehouse.
• Monitor the data warehouse and extract insights to improve data operations, accuracy, quality, coverage, integrity, structure, and usability.
• Turn ambiguous business asks into modeled data. Run requirements gathering with stakeholders, establish grain, surface edge cases, write business rules, and push back on misleading requests.
• Implement measures to improve data quality, accuracy, coverage, lineage, access, and retention across production databases.
• Own the data dictionary, writing table and column documentation tracing each field to its origin. This documentation is used by AI agents and humans.
• Diagnose and resolve data incidents.
• Review teammates’ code to maintain data warehouse hygiene.
• Collaborate with Business Analysts, Data Engineers, Data Scientists, and business process owners to empower data-driven decision-making.
• Enable end users to better understand data complexities, nuances, and limitations.
• Improve agent playbooks, documentation, and tooling for faster team operations.

In the first week, expect to:

• Onboard and integrate into Toptal, learning company history, culture, and vision.
• Set up your environment end-to-end: GCP access, BigQuery, Dataform repositories, and agentic development tooling.
• Shadow teams whose data you will own to understand Toptal’s operations and capabilities.

In the first month, expect to:

• Understand data generated through company operations and its storage.
• Understand ETL processes, timing, tools, monitoring, roadmap, and pain points.
• Understand source systems and their role in business processes.
• Start researching inbound data questions from Business Analysts.
• Build your first Dataform pull request independently and review teammates’ PRs.

In the first three months, expect to:

• Develop mastery of core data elements and entities.
• Standardize definitions and create SQL logic to push definitions into the data layer.
• Be a first responder for data incidents, diagnosing root causes methodically.
• Begin documenting data flows, definitions, calculation methodologies, and data elements.

In the first six months, expect to:

• Contribute architectural ideas impacting data environment and pipelines.
• Be an integral part of the analytics team ensuring a world-class data warehouse service.

In the first year, expect to:

• Play a critical role in setting up the Business Analytics Center of Excellence.
• Be the trusted person about what a number means and whether it can be believed.
• Make the warehouse more usable for analysts and AI agents.

Qualifications and Job Requirements:

• A Bachelor’s degree is required, preferably in Engineering or a related technical field.
• 4+ years of experience in an Analytics Engineer, Data Engineer, or Data Analyst role where you personally shipped production data models.
• Expert-level SQL skills and working knowledge of Python.
• Fluency with LLMs and agentic coding tools, such as Claude Code or Cursor, and understanding of their reliability and verification processes.
• Experience with cloud data warehousing, such as BigQuery or Snowflake.
• Good understanding of a modern transformation framework (dbt, Dataform, SQLMesh, etc.), including dependency graphs, refs, incremental strategies, tests, and environment promotion.
• Git fluency, including branching, pull requests, code review, conflict resolution, and working in a critical production environment.
• Strong familiarity with orchestration tools such as Airflow, Cloud Composer, Dagster, or Prefect.
• Process discipline, including Jira, ticket hygiene, and code review etiquette, with an understanding of avoiding unverified LLM-generated code.
• Experience in exploratory/raw data analysis, data modeling, and data governance (quality, accuracy, coverage, security).
• Experience translating business logic and objectives into SQL code and linking data and analytics to business strategy and operations.
• Familiarity with BI tools such as Tableau or Power BI.
• Detail-oriented, methodical, and thorough.
• Team player who builds strong relationships and collaborates effectively.
• Outstanding written and verbal communication skills, including explaining complex issues simply.
• Ability to work collaboratively and independently, taking ownership of quality, accuracy, and timeliness of deliverables.
• Must be a world-class individual contributor, not just a manager.