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. We are looking for candidates experienced in modern data platforms and analytics engineering. Required skills Strong commercial experience with DBT (Data Build Tool) Advanced knowledge of SQL and data modeling Experience building data transformation pipelines Strong understanding of ELT architecture Experience with incremental models and DBT snapshots Experience writing DBT macros and Jinja templates Understanding of data pipeline performance optimization Experience using Git-based development workflows Nice to have Experience with AWS (Redshift) Experience with Snowflake, BigQuery, or Databricks Experience building global analytics platforms Familiarity with CI/CD pipelines for data engineering Knowledge of data governance and lineage tools Experience working with large-scale data environments Technology stack The platform is built using a modern analytics engineering stack. Typical technologies include: DBT (Data Build Tool) Cloud Data Warehouse SQL Git CI/CD pipelines Data testing and validation frameworks Analytics engineering best practices We offer you: working with the newest machine learning technologies budget on self-development per year possibility to contribute to a variety of interesting projects internal workshops personal branding (articles, conference speaker, internal workshop leader) flexible work hours remote work possibility chillout room / free beverages / team & company event friendly atmosphere MultiSport LuxMed Salary: 20 000 - 28 000 PLN + VAT (B2B) Why is this role interesting: This project processes data coming from over 80 pharmaceutical facilities worldwide, including manufacturing environments and laboratory systems. The platform supports operational insights and data-driven decision-making across global teams. Working on this system means solving challenges related to: large-scale distributed datasets complex transformation pipelines high data quality requirements global analytics infrastructure