Analytics Engineer

About the role:

The Analytics Engineer turns raw product, commercial, and vendor data into trusted data products that teams across Uberall use to make decisions. This role owns the transformation layer of our Databricks lakehouse, from curated models through to the datasets and dashboards stakeholders use every day, and keeps it reliable, documented, and fast.

The work is analytical and creative in equal measure: data products include visual assets, so strong modeling needs to come with a clear sense of how people will actually read and use the data.

What you will own:

The curated data layer

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

Apply dimensional modeling to shape facts and dimensions for analytics

Model transactional sources that originate from CDC and third normal form patterns

Define and enforce naming conventions and project structure

The data consumption layer

Build and maintain data products and datasets for internal stakeholders

Deliver clear, well-designed reporting in Metabase and Pigment

Translate business questions into data models with stakeholders

Enable safe, governed data sharing across teams

Data in production

Own the health of production pipelines orchestrated in Databricks or Apache Airflow

Set up monitoring, alerting, and data quality checks that catch issues early

Manage CI/CD pipelines, schema migrations, and safe releases

Tune performance and cost of workloads on columnar engines

Development patterns, documentation, and governance

Establish reusable development patterns and code review standards

Maintain model documentation, lineage, and column-level metadata

Contribute to governance standards and data contracts with stakeholders and source teams

Support vendor and third-party data ingestion as a secondary focus

What you bring:

Data modeling and transformation

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

Engineering practice

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

Platform and performance

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.

Working with stakeholders

Visualization experience with Metabase, Pigment, or similar tools

Ability to explain technical trade-offs clearly to non-technical colleagues

Plans and delivers complex work independently, with minimal supervision

Raises issues early and comes with proposed solutions

What success looks like

Stakeholders trust the curated data and use it without reconciling numbers themselves

Production pipelines run reliably, and issues surface through alerts rather than stakeholder reports

New data products ship through a consistent, tested, and documented pattern

Models are documented well enough that others can use and extend them confidently

Workloads stay fast and cost-efficient as data volumes grow

Reporting moves off ad-hoc queries and onto the governed data layer

Improvements to the data platform are proposed and driven proactively, not only delivered on request

Why this role matters

Decisions across Uberall depend on consistent, trustworthy data, and this role is where that consistency is built

A strong transformation layer lets the business scale reporting without scaling manual effort

The patterns set here become the foundation the rest of the data team builds on

AI and innovation expectations

Use AI coding assistants to write, test, refactor, and document SQL, dbt, and Python, with human review built into the quality process

Explore AI to automate documentation, data quality checks, and self-serve analytics, so stakeholders get answers faster