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Advanced Data Engineering & dbt Core:

Pipeline Development & Orchestration: Design, implement, and maintain high-performance, automated ELT pipelines using dbt Core (self-hosted via Cloud Run) and BigQuery. Program and orchestrate serverless data flows using GCP Cloud Functions, Cloud Scheduler, and Dataflow (Python).

Data Modeling & SQL Excellence: Develop complex, maintainable SQL solutions and perform advanced data modeling (partitioning, clustering) in BigQuery to reduce query latency and costs, serving as an efficient business-logic alternative to resource-intensive ML overhead.

Infrastructure & API Integration: Provision GCP infrastructure using Infrastructure as Code (Terraform) and integrate external data sources (e.g., Salesforce, SAP BW, Workday, REST APIs) seamlessly into the existing data ecosystem.

System Reliability & Operations: Proactively monitor system health and troubleshoot complex data flows, ensuring high availability and performance of our serverless architecture and automated process chains.

System Administration, GCP Analytics, FinOPs (cloud financial operations) & Security:

FinOps & Performance: Continuously monitor and optimize BigQuery queries (partitioning, clustering) and support strategic Cost Engineering to reduce IT operational costs.

Data Governance & Compliance: Technically implement strict Row-Level Security (RLS) concepts and automated ETL workflows to meet all global Randstad security and data protection standards.

CI/CD (continuous integration & delivery) & Version Control: Actively use our GitLab-based CI/CD setup (Dev, Acc, Prd branches) for seamless and error-free deployments.

Support: Provide 2nd- and 3rd-Level Support.

MLOps (Machine Learning Operations), Legacy Integration & Strategic Development:

MLOps Support: Support the deployment and orchestration of high-precision forecasting models (FbProphet, TBATS, SARIMAX) via Kubeflow and Google Vertex AI.

System Integration: Support data migration (extraction, mapping, testing, and provisioning in GCP models).

Innovation: Leverage modern tools, including Generative AI (e.g., Google Gemini / Prompt Engineering), for code generation and automated technical documentation, and orchestrate event-driven data pipelines using Pub/Sub.

Required Profile & QualificationsEducation: Successfully completed master's or bachelor's degree in Computer Science, Physics, Mathematics, Data Science, or a comparable quantitative field.

Experience: At least 3 years of proven professional experience in cloud-native data engineering, demonstrating the ability to handle complex architectures and enterprise reporting structures independently.

Technical SkillsCore Data Engineering: Deep proficiency in SQL and Python for complex data transformation, automation, and API integrations.

GCP & Orchestration: Expert knowledge of the Google Cloud Platform (BigQuery, Cloud Functions, Cloud Run, Dataflow, Pub/Sub) and robust hands-on experience with self-hosted dbt Core.

Architecture & DevOps: Strong experience provisioning infrastructure via Terraform and managing CI/CD pipelines using GitLab.

Enterprise & MLOps (Nice-to-have): Practical knowledge of SAP BW, SAP Analytics, and Looker Studio. Familiarity with MLOps frameworks like Google Vertex AI and Kubeflow is a strong advantage.

Soft SkillsAnalytical Powerhouse: An analytical mindset with exceptional problem-solving skills, capable of translating complex business requirements into high-performance technical solutions.

Hands-on Mentality: Eagerness to dive deep into the code, refactor legacy systems, and build scalable, future-proof platforms.

Collaborative Spirit: Excellent communication skills, acting as a reliable sparring partner for our Senior/Lead Engineers while successfully consulting with global architects and local stakeholders.

Is this the job for you? We would love to hear from you! Please apply directly to the role and we will get in touch with you.

senior specialist, data science.

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