Senior Data Engineer, FACEIT

• Expert Technical Skills: Mastery of SQL and advanced Python/programming languages for high-performance data processing
• Modern Data Stack (MDS) Expertise: Deep, hands-on experience with cloud data warehouses (e.g., BigQuery internals), workflow orchestration platforms, and dbt optimisation at scale (macros, package management)
• Data Architecture & Infra: Proven track record in dimensional modeling, cloud architecture patterns, Identity and Access Management (IAM), networking, and Infrastructure as Code (IaC)
• AI & Industry Awareness: Substantial awareness of the evolving data ecosystem, combined with practical experience building AI agents and prompting AI effectively
• Project Leadership: Demonstrated success in leading end-to-end projects, mitigating risks within deployment cycles, and managing cross-functional technical delivery
• Communication Excellence: Superb technical storytelling skills; ability to balance deep technical details with strategic business context for both engineering teams and non-technical stakeholders
• Collaboration & Soft Skills: Technical leadership, strategic thinking, ownership mentality, attention to detail, and a passion for enabling self-service and data quality

Desirable

• The Senior Data Engineer is a versatile technical leader who owns the entire end-to-end data lifecycle
• You bridge the gap between raw cloud infrastructure and business intelligence by designing both the extraction pipelines and the warehouse semantic layers
• As a true hybrid engineer, you don’t just move raw data; you transform it into a trusted, scalable, and AI-ready asset
• This role is a critical driver of EFG's core mission centered on Automation, Integrity, and Cognitive Modelling
• End-to-End Architecture & Delivery: Define, design, and implement complex data infrastructure, pipeline ingestion, and transformation layers spanning multiple business units (Esports, Festivals, Commerce, HR, Finance, and FACEIT)
• Pipeline & Platform Engineering: Construct scalable architectures using well-architected framework principles (reliability, security, performance, and automation). Manage workflow orchestration, CI/CD pipelines, and infrastructure automation
• Data Modeling & Semantic Layers: Standardise enterprise-grade dimensional modeling (Kimball/Inmon), star schemas, and centralised semantic layers to define trusted, cross-pillar metrics
• AI-Ready Datasets: Create clean, optimised, AI-ready datasets with structured metadata, clear naming conventions, and explicit documentation to support downstream AI agents and cognitive models
• Technical Standards & Governance: Establish and enforce best practices for Python/SQL development, dbt optimisation, testing frameworks, and version control across the hybrid data domain
• Efficiency & Cost Optimisation: Proactively monitor and optimise performance and infrastructure platform costs across storage, compute, and querying layout (e.g., BigQuery configurations)
• Stakeholder Partnership: Act as a strategic partner to business leaders and analysts, translating complex objectives into scalable self-service data products
• Incident Response & Data Integrity: Lead incident response efforts for both system downtime and data quality anomalies, implementing automated testing to ensure Industry Standard Data Integrity
• Mentorship & Growth: Mentor junior and mid-level engineers through rigorous code reviews and technical guidance, supporting their professional growth and advancing team standards