Engineering Team Lead

We are looking for an experienced engineering leader who combines strong people-management skills with solid software engineering fundamentals and technical judgment. You should be comfortable leading engineers across different technical domains, reviewing code and system designs, and making sound technical decisions without needing to be an expert in every technology.
This role requires balancing short-term delivery with long-term engineering health while providing clear direction and supporting the team’s technical and professional growth.

#Responsibilities
Engineering Leadership & People Management
1. Lead and provide technical direction within the Core team, including Data, Frontend, and AI engineers.
2. Manage, coach, and develop engineers through goal setting, performance management, feedback, and career development.
3. Establish technical priorities and translate Product Owner requirements into practical engineering plans.
4. Review system designs, architecture proposals, prototypes, proofs of concept, and critical code changes.
5. Ensure technical decisions support scalability, maintainability, security, and operational reliability.
6. Balance product delivery, technical debt, platform improvements, and exploratory R&D.
7. Identify technical and delivery risks early and drive issues toward resolution.
8. Evaluate technical opportunities and make informed build, buy, integrate, or discontinue decisions.
9. Support recruitment, interviewing, onboarding, and workforce planning.

Software Quality and Delivery
1. Define and maintain engineering standards for code quality, testing, documentation, security, and release readiness across code, architecture, data pipelines, AI systems, and R&D work.
2. Improve the software development lifecycle, including planning, testing, deployment, monitoring, and incident response.
3. Promote automated testing, CI/CD, observability, and production monitoring while ensuring clear ownership, documentation, and support procedures.
4. Track delivery progress, remove technical or organizational blockers, and work with Product Owners and stakeholders to manage scope, priorities, dependencies, and delivery expectations.
5. Define clear criteria for transitioning experimental work into production.

Data Engineering
1. Guide the architecture and development of data pipelines and platforms supporting back-office systems, dashboards, financial reporting, and BI, including data warehouse, lake, and lakehouse architectures.
2. Ensure data platforms provide accurate, timely, traceable, and well-governed information, with standards for data quality, schema management, lineage, validation, orchestration, and access control.
3. Oversee ETL/ELT technologies such as Python, Airflow, Redshift, Iceberg, and DuckDB.
4. Support data-related research, prototyping, and performance evaluation.

AI Engineering
1. Guide the development of AI-powered systems, including NLP-based chat moderation and future multimodal applications.
2. Define AI evaluation and production standards covering accuracy, latency, false positives/negatives, monitoring, human review, explainability, and fallback mechanisms.
Guide AI research, prototyping, and proof-of-concept development.
3. Ensure AI solutions meet appropriate privacy, fairness, security, and regulatory requirements.

Frontend and Internal Platforms
1. Guide the development of React-based back-office systems for reporting, game management, and operations, promoting reusable architecture, UI consistency, testing, and maintainability.
2. Ensure appropriate authentication, authorization, auditability, and data protection across internal applications and platforms.
3. Lead the technical direction of the internal engineering portal, improving access to documentation, tools, dashboards, services, and system ownership information.
4. Guide frontend prototyping for internal tools and workflows.