About Toptal

Toptal is a global network of top talent in business, design, and technology that enables companies to scale their teams, on-demand. With $200+ million in annual revenue and team members based around the globe, Toptal is the world’s largest fully remote workforce.

We take the best elements of virtual teams and combine them with a support structure that encourages innovation, social interaction, and fun. We see no borders, move at a fast pace, and are never afraid to break the mold.

Job Summary:

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 that is conducive to analytics and business decision making. You will own and maintain the data transformation layer. This will require 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 will coordinate closely with our functional Business Analysts on one side, and Data Engineers on the other.

This role sits within the Business Analytics Center of Excellence and will ensure trustworthy data is available for all downstream data users. Positive relationships with both Data Engineers and Business Analysts will be key, but you must also think independently and bring your own point of view. To be successful in this role you must live and breathe SQL daily and be a critical thinker, problem solver and self-starter.

This role requires an AI-heavy workflow and skillset. We’ve transformed our codebase to be instrumented for agentic development and continue to build our internal AI tooling, and you will be part of this process. Fluency with using LLMs and agentic coding tools is a requirement of this role. The flip side of that is the part machines cannot do: understanding our business and metrics deeply, and making decisions on what matters and what doesn’t in terms of pushing the company’s business objectives forward.

This is a remote position. We do not offer visa sponsorship or assistance. Resumes and communication must be submitted in English.

Responsibilities:

The following information is intended to describe the general nature and level of work being performed. It is not intended to be an exhaustive list of all duties, responsibilities, or required skills.

• Design, write, review and ship SQL models across our repositories that transform raw data into usable data products for all organizational stakeholders. Own and maintain the transformation layer.

• Proactively work with the Data Engineers to ensure new data sources are added and available, and then modeled and published in our data warehouse.

• Proactively monitor the data warehouse and extract insights to identify opportunities to improve data operations, data accuracy and quality, coverage, integrity, structure, and general usability.

• Turn ambiguous business asks into modeled data. Run requirements gathering with stakeholders. Establish the grain, surface the edge cases, write down the business rules, and push back when the request would produce a misleading number.

• Implement measures and processes to improve data quality, accuracy, coverage, lineage, access and retention across dozens of source production databases.

• Own the data dictionary. Write table and column documentation that traces each field to its true origin. This documentation is consumed by AI agents as well as humans, and is an integral part of the semantic layer.

• Diagnose and resolve data incidents.

• Review your teammates’ code and provide feedback to maintain the hygiene of the data warehouse production environment.

• Work closely with Business Analysts, Data Engineers, Data Scientists, and business process owners to empower data-driven decision making.

• Enable end users to better use data, understand complexities, nuances, and limitations.

• Help make the team faster. Improve the agent playbooks, documentation, and tooling the team uses to work with the warehouse.

In the first week, expect to:

• Onboard and integrate into Toptal, and begin learning our history, culture and vision.

• Get your environment running end to end: GCP access, BigQuery, the Dataform repositories, and our agentic development tooling.

• Shadow the teams whose data you will own to learn the core of Toptal’s operations and capabilities, including Growth, Talent Operations, Enterprise, and SMB.

In the first month, expect to:

• Understand the data generated through company operations and activities and where/how that data is stored.

• Understand our ETL processes, timing, tools, monitoring, roadmap, and pain points.

• Understand our source systems, and where they fit into our business processes.

• Start receiving and researching inbound data questions from Business Analysts.

• Build your first Dataform pull request independently and start reviewing your teammates’ PRs.

In the first three months, expect to:

• Develop a mastery of our core data elements and entities.

• Begin standardizing definitions and creating SQL logic to push definitions into the data layer.

• Be a first responder for data incidents, diagnosing root cause with evidence and a methodical approach.

• Begin documenting data flows, definitions, calculation methodologies, and data elements to continuously build and improve the semantic layer.

In the first six months, expect to:

• Contribute architectural ideas that impact our data environment and pipelines. Exercise discretion and independent judgment.

• Be an integral part of the analytics team that ensures a world-class data warehouse service to our stakeholders.

In the first year, expect to:

• Play a critical part in setting up the Business Analytics Center of Excellence for success.

• Be the person the organization trusts about what a number means and whether it can be believed.

• Have made the warehouse meaningfully more usable by both analysts and AI agents.

Qualifications and Job Requirements:

• 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 a working knowledge of Python.

• Fluency with LLMs and agentic coding tools. You already use tools such as Claude Code, Cursor or equivalent as part of your daily working practice. You know where they are reliable, where they should not be used, and how to verify their output.

• Experience with cloud data warehousing, such as BigQuery or Snowflake.

• Good understanding of a modern transformation framework (dbt, Dataform, SQLMesh or similar). You understand dependency graphs, refs, incremental strategies, tests and environment promotion as concepts, not just as commands.

• Git fluency. Branching, pull requests, code review, conflict resolution and generally working with a critical production environment.

• Strong familiarity with orchestration tools such as Airflow, Cloud Composer, Dagster or Prefect. You can read a DAG, understand its schedule and dependencies, and find out why a task failed.

• Process discipline. Jira, ticket hygiene, and code review etiquette. It’s mandatory you understand how to avoid shipping unverified LLM-generated code.

• Experience in doing exploratory/raw data analysis, data modeling, and data governance (quality, accuracy, coverage, security, etc.).

• Experience translating business logic and objectives into SQL code and linking data and analytics to business strategy and operations to drive real impact.

• Familiarity with BI tools (Tableau, Power BI, etc.).

• Detail oriented, methodical, and thorough.

• Team player who builds strong relationships and collaborates with others.

• Outstanding written and verbal communication skills, including the ability to explain complex issues in a simple and intuitive way.

• Ability to work collaboratively and independently; take ownership of quality, accuracy, and timeliness of deliverables.

• You must be a world-class individual contributor to thrive at Toptal. You will not be here just to tell other people what to do.

About Toptal

Toptal is a global network of top talent in business, design, and technology that enables companies to scale their teams, on-demand. With $200+ million in annual revenue and team members based around the globe, Toptal is the world’s largest fully remote workforce.

We take the best elements of virtual teams and combine them with a support structure that encourages innovation, social interaction, and fun. We see no borders, move at a fast pace, and are never afraid to break the mold.

Job Summary:

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 that is conducive to analytics and business decision making. You will own and maintain the data transformation layer. This will require 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 will coordinate closely with our functional Business Analysts on one side, and Data Engineers on the other.

This role sits within the Business Analytics Center of Excellence and will ensure trustworthy data is available for all downstream data users. Positive relationships with both Data Engineers and Business Analysts will be key, but you must also think independently and bring your own point of view. To be successful in this role you must live and breathe SQL daily and be a critical thinker, problem solver and self-starter.

This role requires an AI-heavy workflow and skillset. We’ve transformed our codebase to be instrumented for agentic development and continue to build our internal AI tooling, and you will be part of this process. Fluency with using LLMs and agentic coding tools is a requirement of this role. The flip side of that is the part machines cannot do: understanding our business and metrics deeply, and making decisions on what matters and what doesn’t in terms of pushing the company’s business objectives forward.

This is a remote position. We do not offer visa sponsorship or assistance. Resumes and communication must be submitted in English.

Responsibilities:

The following information is intended to describe the general nature and level of work being performed. It is not intended to be an exhaustive list of all duties, responsibilities, or required skills.

• Design, write, review and ship SQL models across our repositories that transform raw data into usable data products for all organizational stakeholders. Own and maintain the transformation layer.

• Proactively work with the Data Engineers to ensure new data sources are added and available, and then modeled and published in our data warehouse.

• Proactively monitor the data warehouse and extract insights to identify opportunities to improve data operations, data accuracy and quality, coverage, integrity, structure, and general usability.

• Turn ambiguous business asks into modeled data. Run requirements gathering with stakeholders. Establish the grain, surface the edge cases, write down the business rules, and push back when the request would produce a misleading number.

• Implement measures and processes to improve data quality, accuracy, coverage, lineage, access and retention across dozens of source production databases.

• Own the data dictionary. Write table and column documentation that traces each field to its true origin. This documentation is consumed by AI agents as well as humans, and is an integral part of the semantic layer.

• Diagnose and resolve data incidents.

• Review your teammates’ code and provide feedback to maintain the hygiene of the data warehouse production environment.

• Work closely with Business Analysts, Data Engineers, Data Scientists, and business process owners to empower data-driven decision making.

• Enable end users to better use data, understand complexities, nuances, and limitations.

• Help make the team faster. Improve the agent playbooks, documentation, and tooling the team uses to work with the warehouse.

In the first week, expect to:

• Onboard and integrate into Toptal, and begin learning our history, culture and vision.

• Get your environment running end to end: GCP access, BigQuery, the Dataform repositories, and our agentic development tooling.

• Shadow the teams whose data you will own to learn the core of Toptal’s operations and capabilities, including Growth, Talent Operations, Enterprise, and SMB.

In the first month, expect to:

• Understand the data generated through company operations and activities and where/how that data is stored.

• Understand our ETL processes, timing, tools, monitoring, roadmap, and pain points.

• Understand our source systems, and where they fit into our business processes.

• Start receiving and researching inbound data questions from Business Analysts.

• Build your first Dataform pull request independently and start reviewing your teammates’ PRs.

In the first three months, expect to:

• Develop a mastery of our core data elements and entities.

• Begin standardizing definitions and creating SQL logic to push definitions into the data layer.

• Be a first responder for data incidents, diagnosing root cause with evidence and a methodical approach.

• Begin documenting data flows, definitions, calculation methodologies, and data elements to continuously build and improve the semantic layer.

In the first six months, expect to:

• Contribute architectural ideas that impact our data environment and pipelines. Exercise discretion and independent judgment.

• Be an integral part of the analytics team that ensures a world-class data warehouse service to our stakeholders.

In the first year, expect to:

• Play a critical part in setting up the Business Analytics Center of Excellence for success.

• Be the person the organization trusts about what a number means and whether it can be believed.

• Have made the warehouse meaningfully more usable by both analysts and AI agents.

Qualifications and Job Requirements:

• 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 a working knowledge of Python.

• Fluency with LLMs and agentic coding tools. You already use tools such as Claude Code, Cursor or equivalent as part of your daily working practice. You know where they are reliable, where they should not be used, and how to verify their output.

• Experience with cloud data warehousing, such as BigQuery or Snowflake.

• Good understanding of a modern transformation framework (dbt, Dataform, SQLMesh or similar). You understand dependency graphs, refs, incremental strategies, tests and environment promotion as concepts, not just as commands.

• Git fluency. Branching, pull requests, code review, conflict resolution and generally working with a critical production environment.

• Strong familiarity with orchestration tools such as Airflow, Cloud Composer, Dagster or Prefect. You can read a DAG, understand its schedule and dependencies, and find out why a task failed.

• Process discipline. Jira, ticket hygiene, and code review etiquette. It’s mandatory you understand how to avoid shipping unverified LLM-generated code.

• Experience in doing exploratory/raw data analysis, data modeling, and data governance (quality, accuracy, coverage, security, etc.).

• Experience translating business logic and objectives into SQL code and linking data and analytics to business strategy and operations to drive real impact.

• Familiarity with BI tools (Tableau, Power BI, etc.).

• Detail oriented, methodical, and thorough.

• Team player who builds strong relationships and collaborates with others.

• Outstanding written and verbal communication skills, including the ability to explain complex issues in a simple and intuitive way.

• Ability to work collaboratively and independently; take ownership of quality, accuracy, and timeliness of deliverables.

• You must be a world-class individual contributor to thrive at Toptal. You will not be here just to tell other people what to do.