Data Engineering Lead

We are seeking an experienced Principal Data Engineer to lead a team in developing and maintaining robust, scalable data pipelines, bridging on-premises and cloud environments, and delivering real-time analytics systems. This role requires deep expertise in data engineering and streaming technologies, combined with strong leadership skills to drive the team towards achieving business objectives. You will collaborate with cross-functional teams including architecture, product, and software engineering to ensure the delivery of high-quality data solutions aligned with company goals.

Requirements:

• 5+ years of hands-on experience in data engineering, including expertise in Python, Scala, or Java.
• Deep understanding ofApache Kafkafor stream processing workflows (required)
• Proficiency in managing and optimizing databases such as PostgreSQL, MySQL, MSSQL.
• Familiarity with analytical databases.
• Familiarity with both cloud solutions (AWS preferably) andon-premises environmentsas part of cost-optimization efforts.
• Knowledge of additional data tools and frameworks such as Flink, Redis, RabbitMQ, Superset, Cube.js, Minio, and Grafana (optional but beneficial).
• Strong leadership and mentoring skills, with the ability to guide a team and provide technical direction.
• Experience ensuring system reliability, scalability, and data integrity through best practices.
• Experience with ClickHouse or similar technology would be an advantage.
• Familiarity with iGaming industry terminology and challenges is highly preferred.

Responsibilities:

• Provide technical leadership, including making key decisions on solution design, architecture, and implementation strategies.
• Lead and mentor a team of data engineers, serving as the primary point of contact for technical guidance.
• Design and oversee the implementation of scalable, efficient data pipelines and architectures, with a strong focus on stream processing.
• Develop and maintain robust data storage and processing solutions, leveraging tools like Apache Kafka, Redis, and ClickHouse.
• Guide the migration of selected cloud-based solutions to on-premises tools, optimizing costs while maintaining performance and reliability.
• Collaborate with stakeholders to gather requirements, propose designs, and align data strategies with business objectives.
• Ensure system reliability and scalability, with a focus on high availability and robust data transfer mechanisms (e.g., "at least once" delivery).
• Stay up-to-date with emerging technologies and evaluate their potential application to improve the overall data ecosystem.