Описание

This role involves taking end-to-end ownership of data modeling, serving as the architect of the "Source of Truth" that powers the company's most critical strategic decisions, and shipping production code (SQL, dbt, Python) through a Git-based CI/CD workflow.

Responsibilities

Define the Source of Truth by transforming raw, complex data into clean, well-structured tables for high-stakes decision-making
Translate strategy into logic by working with Product, Marketing, and GTM analysts to turn abstract concepts into precise SQL definitions and automated datasets
Maintain data integrity by ensuring consistent, reliable, and logical numbers across product and business reports
Enable High-Tech Initiatives by partnering with AI and Data Service specialists to ensure structured data foundations for building models and external data products
Build scalable models by designing, building, and maintaining production-grade data models using dbt, ensuring code is modular, tested, and documented
Engineer with CI/CD discipline by working through pull requests, automated tests and deployments (GitHub/GitLab), code reviews, and version-controlled environments
Build with AI by using AI coding assistants (Cursor, Claude Code) in daily workflow to speed up modeling, testing, documentation, and code review
Make Data AI-Ready by building well-documented models with clear definitions and a consistent metrics layer
Take part in building Agentic solutions to support 24x7 solutions

Requirements

Experience of 5+ years of prior experience in a relevant analytics/technical role (Analytics Engineer, BI Engineer, BI Developer or Data Engineer)
Expert-level SQL skills for writing complex queries, optimizing performance, and debugging legacy code
Proven hands-on experience with dbt, including Jinja templating, incremental models, and package management
Experience with ETL processes and strong understanding of data warehousing concepts (Amazon Redshift/Snowflake) and modeling methodologies
Understanding of SaaS metrics (ARR, funnels, activation, etc.) and how data logic impacts product growth and strategy
Hands-on experience with Git and CI processes for data projects: pull-request reviews, automated tests on every change, and automated deployments of dbt projects
Practical, day-to-day experience using AI coding tools (Cursor, Claude Code) and the judgment to review and validate AI-generated code
Experience with Python for data orchestration or custom transformations

Nice to have

Experience with an orchestration tool (such as Airflow / Dagster / Prefect) for scheduling and monitoring data workflows
Experience with automated data tests, data contracts, monitoring, and alerting for data quality and observability
Experience building data foundations for LLMs or AI agents, such as semantic/metrics layers, text-to-SQL, or MCP-based data access
Experience tuning queries, models, and warehouse usage to keep the analytical data warehouse fast and cost-efficient

We offer

Competitive compensation
Strong emphasis on well-being
Comprehensive equity packages
Opportunities for team bonding

Hiring process

Apply via Greenhouse
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