Senior Analytics Engineer (DBT Core & Data Modeling)

Remote.AR
Remote.Brazil
Remote.Bulgaria
Remote.Colombia
Remote.Georgia
Remote.Poland
Rosario
Белград
Варна
Варшава
Вроцлав
Ереван
Клуж-Напока
Краков
Ларнака
Лодз
Люблин
Монтевидео
Монтерей
Рига
София
Тбилиси

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Client

Our client is a U.S.-based healthcare marketplace focused on connecting providers with patients to improve access to medical services. The company fosters a strong engineering culture and values proactive individuals who are eager to explore new technologies and implement ready-made solutions.

Position overview

The Data Analytics Engineer will be responsible for understanding business objectives, assessing the current data landscape, and writing SQL queries to transform raw data managed in DBT into comprehensive Looker dashboards.

Effective collaboration requires at least a 3-hour overlap with Eastern Standard Time (EST).

Responsibilities

Build and maintain reliable, scalable data models and pipelines using SQL, DBT, and Dagster
Support self-service analytics by managing tools like Looker and Amplitude, driving adoption and training
Partner with analysts, product teams, and engineers to align data infrastructure with business needs
Improve data ingestion and integration in collaboration with data engineering
Define and promote best practices in data governance, quality, privacy, and security

Requirements

Solid experience with DBT Core to build models, run tests, and deploy changes
Solid experience with data modeling - Kimball, SCDs, Event modeling
Expert-level SQL skills for building performant, production-grade transformations
Proficiency with data visualization tools; experience with Looker is a plus, and mandatory proficiency in any modern BI tool (e.g., Tableau, Power BI, Qlik)
Looker (LookML) for semantic layer design, project architecture, and governance
Python scripting for automation, testing, and developer productivity
Orchestration and workflow management using Dagster (preferred) or Airflow
Experience with Redshift or BigQuery
Amplitude for event-based product analytics and user journey modeling
Implementation of data governance frameworks and KPI management tools to maintain quality, lineage, and ownership
Excellent communication skills for collaborating with cross-functional stakeholders to gather requirements and clarify data context and meaning
Use of AI-assisted development tools (e.g., GitHub Copilot, CodeWhisperer) to improve workflow efficiency
Strong understanding of data modeling and data warehousing principles

Nice to have

Experience with Snowflake as the primary cloud data warehouse

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