Analytics Engineer

Uberall

. Design and build dbt models that transform raw sources into trusted curated data .

Posted 10/25/2026contractRemote • South Africa, Brazil, Armenia, Hungary, Argentina, North MacedoniaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building dbt models, applying dimensional modeling for analytics, and managing production data pipelines in Databricks or Apache Airflow. Proficient in SQL and Python, with a strong focus on data quality, governance, and effective communication with stakeholders.

Highest-signal resume keywords

Dimensional ModelingDbt ExperienceSQL ProficiencyDatabricks OrchestrationCI/CD Pipeline Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Dimensional ModelingDbtSQLPythonData Quality ProcessesColumnar Engine Performance TuningCI/CDSchema MigrationsMonitoring and AlertingData Contracts

Soft Skills

Clear CommunicationProblem-SolvingIndependent WorkProactive Issue Raising

Tools & Technologies

DatabricksApache AirflowMetabasePigmentAWSLinux ShellGit

Industry Keywords

Lakehouse ArchitectureDelta LakeThird Normal Form ModelingVendor Data IngestionSemantic Layers

Tech Stack

Tools & technologies

AirflowApacheAWSLinuxMySQLPostgresPythonSQL

About the role

Key responsibilities & impact

Design and build dbt models that transform raw sources into trusted curated data
Apply dimensional modeling to create facts and dimensions for analytics
Model transactional CDC and third-normal-form sources
Define and enforce naming conventions and project structure
Build and maintain datasets and data products for internal stakeholders
Deliver reporting in Metabase and Pigment
Translate business questions into data models with stakeholders
Enable safe, governed data sharing across teams
Own the health of production pipelines orchestrated in Databricks or Apache Airflow
Set up monitoring, alerting, and data quality checks
Manage CI/CD pipelines, schema migrations, and safe releases
Tune workload performance and costs on columnar engines
Establish reusable development patterns and code review standards
Maintain documentation, lineage, and column-level metadata
Contribute to governance standards and data contracts
Support vendor and third-party data ingestion
Use AI coding assistants to write, test, refactor, and document SQL, dbt, and Python
Explore AI for documentation, data quality checks, and self-serve analytics

Requirements

What you’ll need

Proven experience with dimensional modeling for analytics warehouses
Hands-on production experience with dbt
Understanding of CDC and third normal form modeling for MySQL or PostgreSQL sources
Experience with lakehouse architecture and Delta Lake
Strong SQL and confident, practical Python
Comfort with Git, the Linux shell, and code review workflows
Experience structuring and orchestrating workflows in Databricks or Apache Airflow
Production experience with CI/CD, migrations, monitoring, and alerting
Experience with AWS and Databricks
Demonstrated ability to model for and performance-tune columnar engines
Experience loading data from vendors and third-party sources
Experience with data quality processes
Familiarity with data contracts and semantic layers is a plus
Visualization experience with Metabase, Pigment, or similar tools
Ability to explain technical trade-offs clearly to non-technical colleagues
Ability to plan and deliver complex work independently, with minimal supervision
Ability to raise issues early and propose solutions
English proficiency for day-to-day work and stakeholder communication

Benefits

Comp & perks

100% remote work
Compensation paid in EUR (amount not specified)
AI coding assistants and AI innovation in the workflow
Human review integrated into the quality process
Fully English day-to-day work and stakeholder communication