GCP Analytics Engineer

Description

The company is a global partner for AI-first digital transformation and engineering, with 25 years in the field, 5,000 staff and presence in 16 countries.

It works on AI, data and cloud, intelligent automation and digital products, building scalable products, platforms and data-driven solutions.

The CEE unit has close to 1,000 specialists delivering applications, data platforms and AI solutions for clients including Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea and Schmitz Cargobull, using AWS, Azure, GCP, Databricks and Snowflake.

Responsibilities

• Build, extend and maintain dbt transformation frameworks.
• Create and tune data models for the Silver and Gold layers.
• Improve how dbt runs, in both speed and operational efficiency.
• Manage versions and the lifecycle of dbt artifacts.
• Keep dbt models separate from runtime Docker images and set up controlled ways to distribute them.
• Assess and adopt other execution methods that use native BigQuery features where they fit.
• Merge and streamline data transformation workflows.
• Work alongside Data Engineering, DevOps and Platform teams.
• Help with data quality and transformation governance efforts.
• Write and keep up technical documentation.

Requirements

• Strong hands-on commercial experience with dbt.
• Strong SQL skills.
• Practical experience with BigQuery.
• Experience designing analytical or transformation layers on modern data platforms.
• Experience building and maintaining data models for enterprise analytics.
• Understanding of data model lifecycle management and versioning.
• Experience with Docker-based deployment approaches.
• Familiarity with Python.
• Experience improving the performance and operational efficiency of data transformations.
• Strong communication and documentation skills.
• Experience with Apache Airflow.
• Experience with BigQueryInsertJobOperator or comparable orchestration approaches.
• Familiarity with Google Cloud Storage and Kubernetes.
• Experience building reusable transformation frameworks.
• Experience reducing cloud processing costs.
• Experience in regulated sectors such as banking or financial services.
• Experience supporting data quality and governance work.

Conditions

Hiring runs in stages: CV review, HR call, interview, client interview, then a decision.