At EFG (ESL FACEIT Group) we create worlds beyond gameplay where players and fans become community. We pride ourselves in having a corporate social responsibility which is that “IT’S NOT GG, UNTIL IT’S GG FOR ALL”. We are passionate about the culture we foster that ultimately helps to create and shape the world of esports, gaming tournaments, leagues, events and holistic ecosystems staged for our millions of players, fans and heroes.
Everything we do, from global esports tournaments and community-driven leagues to next-generation platforms and live events, is rooted in our passion, craftsmanship, and culture. With millions of players and fans around the world, we aim to shape the future of esports and gaming by building ecosystems that are inclusive, innovative, and enduring.
The Role
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.
Responsibilities
- 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.
Requirements
- 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.
Bonus skills
- Experience with real-time inference and streaming data architectures;
- Familiarity with MLOps platforms (MLflow, Vertex AI, etc.);
- Understanding of responsible AI, security, and compliance considerations;
- Passionate about learning new, cutting-edge technologies and finding applicable business cases as needed;
- A passion for video games and esports is a plus.
May require occasional travel for team meetings or conferences.
Firmly rooted in our values, EFG is an affirmative action employer that celebrates being an equal opportunity workplace. Our unwavering commitment to fair employment extends to all individuals, regardless of their race, color, ancestry, religion, sex, national origin, age, sexual orientation, disability, citizenship, marital status, gender identity, or Veteran status.
