> DOU

Data Engineer

Zoral

format:Remote

sqlbigquerydbtairflowcloud composerany one ofairflowgoogle cloudcloud storageeltetlany one ofeltdata modelingci/cd

terraformpythonkafkapub/subknowledge graphs

> full description

Role Overview

We are looking for an experienced Data Engineer to design, develop, and maintain scalable data transformation and analytics solutions on Google Cloud Platform (GCP).

The role focuses on building reliable data pipelines and transformation models using BigQuery, dbt, Airflow, and Dataplex, typically following a medallion architecture with bronze, silver, and gold data layers.

The successful candidate should have strong hands-on data engineering skills together with a good understanding of data quality, data cleansing, data modelling, lineage, governance, and production data operations.

Experience extending beyond implementation into data architecture, solution architecture, semantic modelling, or knowledge engineering is highly desirable.

Key Responsibilities

· Develop and maintain data transformation models in dbt and BigQuery.

· Build reliable ELT / ETL pipelines on GCP.

· Design data flows across Bronze, Silver, and Gold layers.

· Implement data cleansing, validation, and data quality controls.

· Orchestrate workflows using Airflow / Cloud Composer.

· Investigate data issues and reconcile discrepancies between source and target systems.

· Work with analysts, architects, and business stakeholders to translate requirements into robust data models.

· Contribute to data modelling, governance, lineage, and platform design decisions.

Required Skills

· Strong SQL and BigQuery experience.

· Hands-on experience with dbt.

· Experience with Airflow / Cloud Composer.

· Good knowledge of GCP data services, ideally including Dataplex and Cloud Storage.

· Strong understanding of ETL / ELT, data modelling, and medallion architecture.

· Good understanding of data quality, cleansing, validation, and reconciliation.

· Experience with Git and CI/CD.

Nice to Have

· Data Architecture or Solution Architecture experience.

· Knowledge Engineering, semantic modelling, ontologies, or knowledge graphs.

· Terraform and Python.

· Kafka / Pub/Sub or streaming architectures.

· Experience with enterprise data platforms or cloud migration projects.

We are particularly interested in engineers who can go beyond implementing pipelines and understand the business meaning, structure, quality, and lifecycle of data.