Xebia
Technology
SeniorAnalyticsEngineer(GCP)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Analytics Engineer (GCP) at Xebia. Skills: Analytics Engineering, GCP, BigQuery, Dbt. Design data models. Build data models”
What You'll Achieve.
Enable self-service analytics; Ensure KPI consistency
Industry & Context.
Data validation; Data testing; Data quality
Work from European Union region, Work permit required
What They're Looking For.
Must Have
Analytics Engineer / Data Engineer experience, Hands-on dbt (or Dataform), SQL-based transformations experience, GCP stack experience, BigQuery experience, Cloud Storage experience, Composer/Airflow experience, SQL skills, Data models building experience, Dimensional modeling experience, Data marts experience, Modern data platforms experience, Lakehouse architectures experience, Data pipelines understanding, ETL/ELT processes understanding, Orchestration understanding, Airflow/Dagster or similar experience, Data validation experience, Data testing experience, Data quality frameworks experience, Stakeholder work experience, Business needs translation experience, Data migration projects familiarity, CI/CD for data experience, Terraform experience, GitHub Actions experience, Cloud Build experience, Data governance knowledge, Data cataloging knowledge, Data lineage tools knowledge, Streaming data processing exposure, Near real-time data processing exposure, AI/ML use cases familiarity, Analytics enablement use cases familiarity, AI-powered assistants practical experience, Work from European Union region, Work permit required
Nice to Have
Knowledge Catalog / data catalog tools experience, BI tools experience, QuickSight or similar experience, Self-service data platforms exposure, FinOps / cost optimization experience, Highly distributed enterprise environments experience
What You'll Do.
Develop analytical datasets
Optimize analytical datasets
Collaborate with data engineers
Collaborate with business stakeholders
Translate requirements
Implement data transformation logic
Ensure data validation
Support pipeline migration
Work with ingestion pipelines
Ensure alignment with reporting needs
Contribute to governance
Contribute to data cataloging
Contribute to lineage
Enable self-service analytics
Validate data consistency
Ensure KPI consistency
Participate in establishing best practices
How You'll Work.
Team & Collaboration
Data engineers; Business stakeholders
Process & Methodology
CI/CD
Full Job Description
Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. You will be: designing, building and maintaining data models (mainly in dbt/Dataform) on top of BigQuery, developing and optimizing analytical datasets and data marts for business consumption, collaborating with data engineers and business stakeholders to translate requirements into scalable data solutions, implementing data transformation logic and ensure data quality, testing, and validation, supporting migration of existing pipelines from AWS / Snowflake / Azure into GCP, working with ingestion pipelines (Pub/Sub, Datastream, Dataflow) and ensure alignment with downstream reporting needs, contributing to governance, data cataloging, and lineage (Knowledge Catalog), enabling self-service analytics through reusable models, templates, and standards, validating data consistency during migration and support cutover (parity vs legacy systems), supporting BI layer (e. g. QuickSight) and ensure consistency of KPIs across domains, participating in establishing best practices for analytics engineering and data product
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