DBT Expert / Senior Analytics Engineer / Data Engineer

You’ll be joining a team of passionate developers to work on projects for local and international companies as well as in academic research. This will involve different phases of the end-to-end delivery – direct contact with the client, business analysis of the problem, coming up with an appropriate solution, implementation and moving it to the production environment. You’ll be directly cooperating with machine learning team members, either by leading the project or assisting in various stages.

Stermedia.ai is looking for an experienced DBT Expert / Senior Analytics Engineer to join a data platform team building a global analytics environment for a leading pharmaceutical organization.

The platform integrates data from more than 80 facilities worldwide, including laboratories, manufacturing systems, logistics platforms, and operational environments. The goal is to transform distributed datasets into reliable, analytics-ready models that support reporting, operational monitoring, and data-driven decision making.

You will work with a modern analytics stack, designing scalable transformation pipelines using DBT (Data Build Tool) and cloud data warehouse technologies.

About the project

The project focuses on building a scalable data platform that processes operational data coming from pharmaceutical laboratories, production systems, and global supply chains.

Data flows continuously from multiple regions and is consolidated into a centralized analytics environment. The transformation layer is implemented using DBT, allowing teams to create reliable, maintainable, and well-documented data models.

As a DBT expert, you will be responsible for designing transformation pipelines, maintaining clean DAG structures, and ensuring high data quality across the platform.

Responsibilities

In this role, you will focus on building and maintaining scalable data transformations.

Your responsibilities will include:

• Designing and maintaining DBT models for large-scale data pipelines

• Building structured transformation layers, including staging, intermediate models, and data marts

• Creating reusable DBT macros and Jinja-based transformations

• Implementing data tests and data quality checks

• Optimizing performance of incremental models and large datasets

• Maintaining a clean DBT dependency graph (DAG)

• Supporting data lineage and documentation

Collaborating with international data engineering and analytics teams.