[Hiring] Data Analytics Engineer @Toptal

Role Description

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
• 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.

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

Responsibilities

• 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.
• 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.

Expectations

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

• 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.
• Experience with cloud data warehousing, such as BigQuery or Snowflake.
• Good understanding of a modern transformation framework (dbt, Dataform, SQLMesh or similar).
• 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.
• Process discipline. Jira, ticket hygiene, and code review etiquette.
• Experience in doing exploratory/raw data analysis, data modeling, and data governance.
• Experience translating business logic and objectives into SQL code.
• 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.
• Ability to work collaboratively and independently; take ownership of quality, accuracy, and timeliness of deliverables.